#Background At Blue Oak Ranch Reserve, we established a 10m x 26m fenced field site to test whether evolution over the course of invasion away from roads has resulted in enhanced performance in undisturbed vegetation relative to roadside populations. The experiment was replicated in a randomized block design (20 plots in total). Each plot was 1.5m2 with 16 D. graveolens growing in a 4 x 4 grid centered on the plot. There was 33cm between each plant and 25cm between the edge plants and the border of the plot.

The experiment included multiple treatments; however, only the two most relevant to the focus of this paper are included here. We tested whether plant genotypes collected from the two habitats responded differently to the disturbance of biomass removal. We tilled in December 2020 to completely remove below and aboveground biomass, and then weeded to remove aboveground biomass throughout the growing season. In contrast, we left the control plots untouched, allowing the previous year’s thatch to persist and background vegetation to grow throughout the experiment.

In January 2021, we germinated seeds in Petri dishes at the UCSC Coastal Science Campus greenhouse incubation chambers before transplanting them into field-collected soil (collected in late December 2020 from Blue Oak Ranch Reserve). Seedlings grew in the greenhouse for about eight weeks until all plants had their first two true leaves emerge and lengthen. Ideally, we would have placed seeds directly into the field, but to maximize biosafety, we used seedling transplants that could be tracked with 100% certainty.

We measured the longest leaf for each plant and then transplanted them into the ground in late February 2021 at Blue Oak Ranch Reserve. During the first month of growth, we replaced any D. graveolens that died. We conducted weekly phenology surveys to assess D. graveolens plant health, and at the first sign of buds, we measured plant height and harvested the aboveground biomass by cutting at the root crown and drying in a 60ºC oven for 3 days before weighing.

#Data Analysis Statistical analyses were performed in R version 4.2.1 (R Core Team 2022) using linear mixed-effects models with the lme4 (Bates et al. 2015), lmerTest (Kuznetsova et al. 2017), and DHARMa packages (Hartig 2022), generalized linear mixed models with the glmmTMB package (Brooks et al. 2017), and mixed effects cox models with the coxme (Therneau 2022a) and survival (Therneau 2022b) packages.

##Models Included

###Cox proportional hazard models Here we will use ‘coxme’ which allows you to conduct mixed effects Cox proportional hazards models. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. We conducted a germination experiment using Dittrichia graveolens seeds on filter paper. “Surv” creates a survival object to combine the days column (NumDaysAlive) and the censor column (Censor) to be used as a response variable in a model formula.The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: “var1 * var2” will give you the interaction term and the individual variables: so “var1 + var2 + var1:var2”. To add random effects, type “+ (1|random effect)”.

Assumptions for cox models: https://www.theanalysisfactor.com/assumptions-cox-regression/

###Linear mixed-effects models Linear Mixed Effects models are used for regression analyses involving dependent data. For a tutorial: https://doi.org/10.1177/2515245920960351

fullmodel<-lmer(log(Biomass)~ #Response variable: biomass HabitatTreatment+ #Fixed effects and their interactions() (1|Site)+(1|Block), #Random effect with random intercept only data=mydata) #Dataframe

###Generalized linear mixed models under construction

#Libraries

#install.packages("coxme")
#install.packages("survival")
#install.packages("ggplot2")
#install.packages("ggfortify")
#install.packages("car")
#install.packages("multcomp")
#install.packages("lme4")
#install.packages("lmerTest")
#install.packages("DHARMa")
#install.packages("dplyr")
#install.packages("emmeans")
#install.packages('TMB', type = 'source')
#install.packages("glmmTMB")
#install.packages("MASS")
#install.packages("emmeans")
#install.packages("AICcmodavg")
library(coxme)
library(survival)
library(ggplot2)
library(ggfortify)
library(car)
library(multcomp)
library(lme4)
library(lmerTest)
library(DHARMa)
library(dplyr)
library(emmeans)
library(TMB)
library(glmmTMB)
library(MASS)
library(emmeans)
library(AICcmodavg)

#Load Data This dataframe has one row per plant (800 observations). Data are for survivorship curves (3 censor options), the number of days the plant stayed alive (NumDaysAlive) and aboveground biomass. Censors with a 1 denote reaching the event (CensorAll = died, CensorBiomass = survived to collect biomass, CensorReproduction = survived to reproduce) and a 0 denoting when a seed didn’t germinate by the last census date (Census = 11/15/21). CensorReproduction will be most useful in understanding the amount of biomass produced by an individual when buds appear.

str(mydata) #Check that each column has the right class (factor, integer, numeric, etc.)
'data.frame':   800 obs. of  29 variables:
 $ Block             : Factor w/ 10 levels "A","B","C","D",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ Plot              : int  5 2 2 1 5 1 1 4 4 2 ...
 $ Flag              : Factor w/ 50 levels "A1","A2","A3",..: 5 7 12 16 25 26 31 39 44 47 ...
 $ Pos               : int  3 15 13 13 6 10 8 4 12 3 ...
 $ Flag_Pos          : Factor w/ 800 levels "A1_01","A1_02",..: 67 111 189 253 390 410 488 612 700 739 ...
 $ Population        : Factor w/ 16 levels "BAY-A","BAY-O",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Site              : Factor w/ 8 levels "BAY","CHE","GUA",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Habitat           : Factor w/ 2 levels "Roadside","Vegetated": 1 1 1 1 1 1 1 1 1 1 ...
 $ Treatment         : Factor w/ 5 levels "Biomass Removal",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ PlantDate         : Factor w/ 8 levels "2/27/21","3/1/21",..: 5 1 1 1 1 1 8 1 1 1 ...
 $ LeafMeas1         : num  30 20.3 27 14.9 16.7 ...
 $ LeafMeas2         : int  46 51 NA 56 42 45 36 58 52 45 ...
 $ Growth            : num  16 30.7 -27 41 25.3 ...
 $ MortDate          : Factor w/ 29 levels "","10/1/21","11/14/21",..: 1 25 7 1 1 1 1 1 1 1 ...
 $ MortHarvDate      : Factor w/ 33 levels "10/1/21","10/28/21",..: 21 27 9 29 20 29 29 29 30 30 ...
 $ CensorAll         : int  1 1 1 1 1 1 1 1 1 1 ...
 $ DaysMort          : int  105 181 62 195 139 195 185 195 198 198 ...
 $ Census            : Factor w/ 1 level "11/15/21": 1 1 1 1 1 1 1 1 1 1 ...
 $ DaysCensus        : int  241 261 261 261 261 261 251 261 261 261 ...
 $ NumDaysAlive      : int  105 181 62 195 139 195 185 195 198 198 ...
 $ HarvDate          : Factor w/ 22 levels "","10/1/21","10/28/21",..: 8 15 1 17 7 17 17 17 18 18 ...
 $ CensorBiomass     : int  1 1 0 1 1 1 1 1 1 1 ...
 $ BudDate           : Factor w/ 20 levels "","10/1/21","10/28/21",..: 9 1 1 17 11 17 17 17 17 17 ...
 $ CensorReproduction: int  1 0 0 1 1 1 1 1 1 1 ...
 $ PropBud           : num  0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 ...
 $ PropBudSite       : num  0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
 $ Height            : num  12.4 33.8 NA 56.6 21.5 55.2 45.2 37.5 50.8 48.5 ...
 $ Biomass           : num  0.762 7.048 NA 7.803 2.572 ...
 $ Biomass.date      : Factor w/ 18 levels "","0.561","10/13/21",..: 17 16 1 8 15 11 11 11 10 10 ...

#Early Growth This code uses Growth data with Habitat (roadside and vegetated) and Treatment in a glmm model. Anova and Tukey tests are used on the successful Model4 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Growth data is a measure of growth of longest leaf. Each plant was measured upon transplanting into the field in March and then again in June 2021. This plant has a juvenile stage of a basal rosette, and then it bolts and produced smaller cauline leaves. The goal was to capture early growth data for this plant before bolting occurs so that the measurements capture the basal rosette stage, however in some cases the plants bolted earlier than expected and the resulting measurement was smaller than previous. In these cases we determined that the data should be removed from the dataset as the negative number (or changing it to a zero) does not reflect the biological importance of the measurement.

Here we need to filter the data to remove Growth>0 and to convert plant measurement dates to date format

growth_mydata<-mydata%>%filter(Growth>0) #Here I am only looking at the Growth data that is greater than 0 (see Also Note above)
growth_mydata$PlantDate<-as.Date(growth_mydata$PlantDate,"%m/%d/%y") 
growth_mydata$Num.Days.Growth<-as.Date("2021-05-22")-growth_mydata$PlantDate
growth_mydata$Num.Days.Growth<-as.numeric(growth_mydata$Num.Days.Growth)
growth_mydata$Growth.Rate<-growth_mydata$Growth/growth_mydata$Num.Days.Growth #First calculate number of days of growth to get the Rate (Growth/Num.Days.Growing). Then fit data to Beta distribution

##Histograms Original data

hist(growth_mydata$Growth.Rate,
     col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data

shapiro.test(growth_mydata$Growth.Rate)

    Shapiro-Wilk normality test

data:  growth_mydata$Growth.Rate
W = 0.90445, p-value < 2.2e-16

Log transform data (https://www.statology.org/transform-data-in-r/)

log_growth_mydata<-log10(growth_mydata$Growth.Rate)
hist(log_growth_mydata,
     col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution

shapiro.test(log_growth_mydata) #Data does not improve with log transformation

    Shapiro-Wilk normality test

data:  log_growth_mydata
W = 0.92178, p-value = 1.503e-15

Next I tried a square root transformation, which improved the distribution, but my first model failed the singular fit (see Model 1).

sqrt_growth_mydata<-sqrt(growth_mydata$Growth.Rate)
hist(sqrt_growth_mydata,
     col='steelblue',main='Log Transformed') #Square root transformed data looks better than the original distribution

shapiro.test(sqrt_growth_mydata)

    Shapiro-Wilk normality test

data:  sqrt_growth_mydata
W = 0.98581, p-value = 7.695e-05

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects

growth_fullmodel1<-lmer(sqrt(Growth.Rate)~Habitat*Treatment+(1|Site)+(1|Block),data=growth_mydata)
boundary (singular) fit: see help('isSingular')
isSingular(growth_fullmodel1,tol=1e-4) #=True
[1] TRUE
summary(growth_fullmodel1) #Variance explained by Site = 0.000
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sqrt(Growth.Rate) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
   Data: growth_mydata

REML criterion at convergence: -583.3

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.61595 -0.69539  0.05978  0.73053  2.88562 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.001598 0.03998 
 Site     (Intercept) 0.000000 0.00000 
 Residual             0.016171 0.12716 
Number of obs: 506, groups:  Block, 10; Site, 8

Fixed effects:
                                              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)                                   0.271164   0.024328  78.070400  11.146  < 2e-16
HabitatVegetated                              0.023733   0.028530 487.902365   0.832  0.40590
TreatmentBiomass Removal                      0.299248   0.025549 488.988743  11.713  < 2e-16
TreatmentHemizonia                            0.009046   0.029694 488.862262   0.305  0.76078
TreatmentRaking                               0.037015   0.028063 487.757926   1.319  0.18778
TreatmentRaking & Clipping                    0.073071   0.027082 488.008833   2.698  0.00722
HabitatVegetated:TreatmentBiomass Removal    -0.005098   0.035439 487.461999  -0.144  0.88568
HabitatVegetated:TreatmentHemizonia           0.014342   0.041419 487.869509   0.346  0.72928
HabitatVegetated:TreatmentRaking             -0.022588   0.038582 488.126281  -0.585  0.55852
HabitatVegetated:TreatmentRaking & Clipping  -0.006018   0.037436 488.124609  -0.161  0.87236
                                               
(Intercept)                                 ***
HabitatVegetated                               
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                                
TreatmentRaking & Clipping                  ** 
HabitatVegetated:TreatmentBiomass Removal      
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.614                                                        
TrtmntBmssR -0.694  0.584                                                 
TretmntHmzn -0.596  0.501  0.567                                          
TretmntRkng -0.626  0.532  0.597  0.512                                   
TrtmntRkn&C -0.650  0.551  0.619  0.533  0.560                            
HbttVgt:TBR  0.493 -0.804 -0.714 -0.402 -0.428 -0.442                     
HbttVgtt:TH  0.418 -0.687 -0.398 -0.708 -0.365 -0.378  0.553              
HbttVgtt:TR  0.454 -0.741 -0.432 -0.369 -0.726 -0.406  0.596  0.508       
HbttVg:TR&C  0.465 -0.761 -0.442 -0.381 -0.403 -0.721  0.612  0.526  0.564
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
anova(growth_fullmodel1)
Type III Analysis of Variance Table with Satterthwaite's method
                  Sum Sq Mean Sq NumDF  DenDF  F value  Pr(>F)    
Habitat           0.0464 0.04642     1 488.79   2.8709 0.09083 .  
Treatment         7.4582 1.86456     4 489.79 115.3056 < 2e-16 ***
Habitat:Treatment 0.0146 0.00366     4 487.95   0.2260 0.92382    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Site as a random effect does not explain any of the variance in the model, therefore let’s try Site as a fixed effect to see if it adds to the model.

##Model 2 - lmer: Modeling Site as a fixed effect and Block as a random effect

growth_fullmodel2<-lmer(log(Growth.Rate)~
                          Habitat*Treatment+
                          Site+
                          (1|Block),
                        data=growth_mydata)
summary(growth_fullmodel2) #As a fixed effect, one of the Sites (OAP) is significant.
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Growth.Rate) ~ Habitat * Treatment + Site + (1 | Block)
   Data: growth_mydata

REML criterion at convergence: 1374.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-6.2000 -0.4782  0.1779  0.6492  1.8316 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.06789  0.2606  
 Residual             0.82580  0.9087  
Number of obs: 506, groups:  Block, 10

Fixed effects:
                                              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)                                  -2.984938   0.204110 165.530302 -14.624  < 2e-16
HabitatVegetated                              0.220653   0.204078 480.955621   1.081  0.28014
TreatmentBiomass Removal                      1.729151   0.183091 482.054827   9.444  < 2e-16
TreatmentHemizonia                            0.196272   0.212666 481.942503   0.923  0.35651
TreatmentRaking                               0.346745   0.201477 480.773125   1.721  0.08589
TreatmentRaking & Clipping                    0.547314   0.194472 480.894171   2.814  0.00509
SiteCHE                                       0.039617   0.163190 481.278240   0.243  0.80829
SiteGUA                                      -0.006678   0.163292 481.229118  -0.041  0.96740
SiteLEX                                      -0.063730   0.164176 481.225590  -0.388  0.69805
SiteOAP                                       0.329379   0.161428 481.228603   2.040  0.04186
SitePAR                                       0.105935   0.165141 480.587844   0.641  0.52152
SitePEN                                       0.115262   0.167219 481.510246   0.689  0.49098
SiteSSJ                                       0.112886   0.166554 480.788469   0.678  0.49824
HabitatVegetated:TreatmentBiomass Removal    -0.152103   0.253524 480.438799  -0.600  0.54882
HabitatVegetated:TreatmentHemizonia           0.048304   0.296429 480.886893   0.163  0.87062
HabitatVegetated:TreatmentRaking             -0.214508   0.276049 481.225843  -0.777  0.43750
HabitatVegetated:TreatmentRaking & Clipping  -0.193752   0.268159 481.243075  -0.723  0.47032
                                               
(Intercept)                                 ***
HabitatVegetated                               
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                             .  
TreatmentRaking & Clipping                  ** 
SiteCHE                                        
SiteGUA                                        
SiteLEX                                        
SiteOAP                                     *  
SitePAR                                        
SitePEN                                        
SiteSSJ                                        
HabitatVegetated:TreatmentBiomass Removal      
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation matrix not shown by default, as p = 17 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it
anova(growth_fullmodel2) #Site as a fixed effect accounts for 3% of the variance
Type III Analysis of Variance Table with Satterthwaite's method
                   Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)    
Habitat             1.636   1.636     1 482.01  1.9816 0.1599    
Treatment         211.245  52.811     4 483.04 63.9513 <2e-16 ***
Site                6.623   0.946     7 481.06  1.1457 0.3331    
Habitat:Treatment   1.190   0.297     4 481.03  0.3601 0.8370    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Now we’ll look at the QQ plots and the residuals using DHARMa

qqnorm(resid(growth_fullmodel2)) #qqplot
qqline(resid(growth_fullmodel2)) #add the line

testDispersion(growth_fullmodel2) #red line should be in the middle of the distribution

    DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated

data:  simulationOutput
dispersion = 0.97037, p-value = 0.68
alternative hypothesis: two.sided

myDHARMagraph2<-simulateResiduals(growth_fullmodel2) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph2) #plotting graph. At this point, you don't want any text or lines to be red. The QQ plots are not looking good for this model. Let's try fitting to a negative binomial distribution.

##Model 3 - glmer.nb: Negative Binomial

So this model isn’t working well either. Let’s try building a model and fitting it to a Beta distribution, first without a link function. Check it with DHARMa, and if it doesn’t look good, then fit it to a Beta distribution with a logit link function.

##Model 4 - glmm: Beta Distribution Now I’m using glmm because I’m fitting to other distributions (beta)

growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata) 
summary(growth_fullmodel4)
 Family: beta  ( logit )
Formula:          Growth.Rate ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata

     AIC      BIC   logLik deviance df.resid 
  -989.3   -934.4    507.7  -1015.3      493 

Random effects:

Conditional model:
 Groups Name        Variance  Std.Dev.
 Site   (Intercept) 0.0002645 0.01626 
 Block  (Intercept) 0.0429336 0.20720 
Number of obs: 506, groups:  Site, 8; Block, 10

Dispersion parameter for beta family (): 11.4 

Conditional model:
                                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                                 -2.27681    0.14929 -15.251   <2e-16 ***
HabitatVegetated                             0.12961    0.17864   0.726    0.468    
TreatmentBiomass Removal                     1.60566    0.15070  10.655   <2e-16 ***
TreatmentHemizonia                           0.07352    0.18737   0.392    0.695    
TreatmentRaking                              0.20030    0.17566   1.140    0.254    
TreatmentRaking & Clipping                   0.41358    0.16659   2.483    0.013 *  
HabitatVegetated:TreatmentBiomass Removal   -0.03513    0.20401  -0.172    0.863    
HabitatVegetated:TreatmentHemizonia          0.10730    0.25788   0.416    0.677    
HabitatVegetated:TreatmentRaking            -0.09786    0.23867  -0.410    0.682    
HabitatVegetated:TreatmentRaking & Clipping -0.06623    0.22709  -0.292    0.771    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
qqnorm(resid(growth_fullmodel4)) #qqplot
qqline(resid(growth_fullmodel4)) #add the line

testDispersion(growth_fullmodel4) #red line should be in the middle of the distribution

    DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated

data:  simulationOutput
dispersion = 0.97597, p-value = 0.84
alternative hypothesis: two.sided

myDHARMagraph4<-simulateResiduals(growth_fullmodel4) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph4) #plotting graph. Looks good, but check the outliers to make sure they are real.

We should check the outliers in the the DHARMa plot to make sure they make sense.

growth_outlier_boxplot1<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Habitat,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Habitat")
growth_outlier_boxplot1


max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Away"])
Warning: no non-missing arguments to max; returning -Inf
[1] -Inf
max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Roadside"]) #Yes, these outliers make sense, so I don't need to worry about the red stars in the DHARMa plot.
[1] 0.75

###Best Model

growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata)
summary(growth_fullmodel4)
 Family: beta  ( logit )
Formula:          Growth.Rate ~ Habitat * Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata

     AIC      BIC   logLik deviance df.resid 
  -989.3   -934.4    507.7  -1015.3      493 

Random effects:

Conditional model:
 Groups Name        Variance  Std.Dev.
 Site   (Intercept) 0.0002645 0.01626 
 Block  (Intercept) 0.0429336 0.20720 
Number of obs: 506, groups:  Site, 8; Block, 10

Dispersion parameter for beta family (): 11.4 

Conditional model:
                                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                                 -2.27681    0.14929 -15.251   <2e-16 ***
HabitatVegetated                             0.12961    0.17864   0.726    0.468    
TreatmentBiomass Removal                     1.60566    0.15070  10.655   <2e-16 ***
TreatmentHemizonia                           0.07352    0.18737   0.392    0.695    
TreatmentRaking                              0.20030    0.17566   1.140    0.254    
TreatmentRaking & Clipping                   0.41358    0.16659   2.483    0.013 *  
HabitatVegetated:TreatmentBiomass Removal   -0.03513    0.20401  -0.172    0.863    
HabitatVegetated:TreatmentHemizonia          0.10730    0.25788   0.416    0.677    
HabitatVegetated:TreatmentRaking            -0.09786    0.23867  -0.410    0.682    
HabitatVegetated:TreatmentRaking & Clipping -0.06623    0.22709  -0.292    0.771    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

###Post-Hoc Test Remove non-significant interaction terms before running the Tukey.

#?emmeans,emmeans(model,pairwise~treatment)
growth_fullmodel4.1<-glmmTMB(Growth.Rate~
                               Treatment+
                               (1|Site)+
                               (1|Block),
                             family=beta_family(),
                             data=growth_mydata)
summary(growth_fullmodel4.1)
 Family: beta  ( logit )
Formula:          Growth.Rate ~ Treatment + (1 | Site) + (1 | Block)
Data: growth_mydata

     AIC      BIC   logLik deviance df.resid 
  -996.0   -962.2    506.0  -1012.0      498 

Random effects:

Conditional model:
 Groups Name        Variance  Std.Dev.
 Site   (Intercept) 0.0006051 0.0246  
 Block  (Intercept) 0.0416553 0.2041  
Number of obs: 506, groups:  Site, 8; Block, 10

Dispersion parameter for beta family (): 11.3 

Conditional model:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 -2.2066     0.1144 -19.284  < 2e-16 ***
TreatmentBiomass Removal     1.5835     0.1053  15.043  < 2e-16 ***
TreatmentHemizonia           0.1210     0.1295   0.934  0.35019    
TreatmentRaking              0.1507     0.1203   1.253  0.21035    
TreatmentRaking & Clipping   0.3781     0.1150   3.287  0.00101 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
emmeans(growth_fullmodel4.1,
        pairwise~Treatment)
$emmeans
 Treatment         emmean     SE  df lower.CL upper.CL
 Grassland         -2.207 0.1144 498   -2.431   -1.982
 Biomass Removal   -0.623 0.0818 498   -0.784   -0.462
 Hemizonia         -2.086 0.1158 498   -2.313   -1.858
 Raking            -2.056 0.1050 498   -2.262   -1.850
 Raking & Clipping -1.828 0.0969 498   -2.019   -1.638

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$contrasts
 contrast                            estimate     SE  df t.ratio p.value
 Grassland - Biomass Removal          -1.5835 0.1053 498 -15.043  <.0001
 Grassland - Hemizonia                -0.1210 0.1295 498  -0.934  0.8835
 Grassland - Raking                   -0.1507 0.1203 498  -1.253  0.7203
 Grassland - Raking & Clipping        -0.3781 0.1150 498  -3.287  0.0095
 Biomass Removal - Hemizonia           1.4625 0.1071 498  13.658  <.0001
 Biomass Removal - Raking              1.4327 0.0950 498  15.081  <.0001
 Biomass Removal - Raking & Clipping   1.2054 0.0864 498  13.956  <.0001
 Hemizonia - Raking                   -0.0298 0.1225 498  -0.243  0.9992
 Hemizonia - Raking & Clipping        -0.2571 0.1170 498  -2.198  0.1822
 Raking - Raking & Clipping           -0.2274 0.1062 498  -2.141  0.2043

Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 5 estimates 

###ggplot ####Interaction plot #####Biomass x Treatment: Growth

pd<-position_dodge(0)

growth_mydata1<-growth_mydata%>% 
  replace_na(list(Biomass=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Biomass),
            SD=sd(Biomass),
            N=length(Biomass))%>%
  mutate(SE=SD/sqrt(N))
`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.
field_int_bio_grow_gg<-ggplot(growth_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Biomass (g)")+
  coord_cartesian(ylim = c(0,10))+
  xlab("")+
  #ggtitle("Biomass of Dittrichia graveolens that budded")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="*Dittrichia graveolens* Biomass (g)",
       color="Source Habitat",
       shape="Source Habitat")
field_int_bio_grow_gg

ggsave(plot=field_int_bio_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_bio_grow_gg.png",width=25,height=15,units="cm",dpi=800)

#####Growth Rate x Treatment: Growth

pd<-position_dodge(0)

growth_mydata2<-growth_mydata%>% 
  replace_na(list(Growth.Rate=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Growth.Rate),
            SD=sd(Growth.Rate),
            N=length(Growth.Rate))%>%
  mutate(SE=SD/sqrt(N))
`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.
field_int_rate_grow_gg<-ggplot(growth_mydata2,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  coord_cartesian(ylim = c(0,0.5))+
  xlab("")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
 # ggtitle("Growth of Dittrichia graveolens that budded")+
  ylab("*Dittrichia graveolens* \n (Change in Leaf Length/Days)")+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Change in *Dittrichia graveolens* Leaf Length/Day",
       color="Source Habitat",
       shape="Source Habitat")
field_int_rate_grow_gg

ggsave(plot=field_int_rate_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_rate_grow_gg.png",width=25,height=15,units="cm",dpi=800)

####Boxplot

#Reproductive Biomass This code uses Biomass data with Habitat (roadside and vegetated) and Treatment in a lmer model. ANOVA and Tukey are used on the successful Model 3 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Biomass data is a measure of aboveground biomass of all individuals harvested from the site. In some cases this was before they budded, but we harvested the wilted plant in case that information was needed in the future. Here we only want to look at biomass (well, log(Biomass)) for reproductive individuals.

Here we need to subset the data to only look at Biomass when CensorReproduction = 1, so that all Biomass data is for reproductive individuals only.

reproduction_mydata<-subset(mydata,CensorReproduction%in%c('1')) #Here I am only looking at the Biomass data where the CensorReproduction = 1

##Histograms

hist(reproduction_mydata$Biomass,col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data

shapiro.test(reproduction_mydata$Biomass)

    Shapiro-Wilk normality test

data:  reproduction_mydata$Biomass
W = 0.62548, p-value < 2.2e-16

Log transform data (https://www.statology.org/transform-data-in-r/)

log_reproduction_mydata<-log10(reproduction_mydata$Biomass)
hist(log_reproduction_mydata,col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution

shapiro.test(log_reproduction_mydata)

    Shapiro-Wilk normality test

data:  log_reproduction_mydata
W = 0.9648, p-value = 9.101e-06
#Log transformed data has a better distribution than the original data so we will use the log transformed data with our models

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects

fullmodel1<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Site)+
                   (1|Block),
                 data=reproduction_mydata)
isSingular(fullmodel1,tol=1e-4)
[1] FALSE
summary(fullmodel1) #Variance explained by Site = 0.000
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Site) + (1 | Block)
   Data: reproduction_mydata

REML criterion at convergence: 675.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2251 -0.6836  0.0555  0.6095  2.9640 

Random effects:
 Groups   Name        Variance  Std.Dev.
 Block    (Intercept) 0.1836330 0.42852 
 Site     (Intercept) 0.0003818 0.01954 
 Residual             0.8322599 0.91228 
Number of obs: 247, groups:  Block, 10; Site, 8

Fixed effects:
                                             Estimate Std. Error        df t value Pr(>|t|)
(Intercept)                                  -1.47119    0.25189  65.18355  -5.841  1.8e-07
HabitatVegetated                              0.52617    0.31275 229.17635   1.682   0.0939
TreatmentBiomass Removal                      3.16732    0.24296 227.53081  13.036  < 2e-16
TreatmentHemizonia                           -0.05905    0.31382 229.47907  -0.188   0.8509
TreatmentRaking                               0.25358    0.41060 211.72838   0.618   0.5375
TreatmentRaking & Clipping                    0.54416    0.31428 229.25370   1.731   0.0847
HabitatVegetated:TreatmentBiomass Removal    -0.59897    0.35315 229.14685  -1.696   0.0912
HabitatVegetated:TreatmentHemizonia          -0.30666    0.44327 228.66064  -0.692   0.4898
HabitatVegetated:TreatmentRaking             -0.20197    0.55618 220.56602  -0.363   0.7168
HabitatVegetated:TreatmentRaking & Clipping  -0.79028    0.43205 229.37117  -1.829   0.0687
                                               
(Intercept)                                 ***
HabitatVegetated                            .  
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                                
TreatmentRaking & Clipping                  .  
HabitatVegetated:TreatmentBiomass Removal   .  
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564                                                        
TrtmntBmssR -0.738  0.582                                                 
TretmntHmzn -0.558  0.439  0.582                                          
TretmntRkng -0.445  0.351  0.463  0.342                                   
TrtmntRkn&C -0.570  0.455  0.591  0.450  0.361                            
HbttVgt:TBR  0.500 -0.883 -0.675 -0.390 -0.309 -0.403                     
HbttVgtt:TH  0.392 -0.702 -0.404 -0.690 -0.246 -0.316  0.619              
HbttVgtt:TR  0.319 -0.556 -0.331 -0.252 -0.723 -0.258  0.490  0.391       
HbttVg:TR&C  0.413 -0.727 -0.425 -0.316 -0.262 -0.725  0.641  0.507  0.405
anova(fullmodel1)
Type III Analysis of Variance Table with Satterthwaite's method
                  Sum Sq Mean Sq NumDF  DenDF  F value Pr(>F)    
Habitat             0.84   0.843     1 229.56   1.0133 0.3152    
Treatment         483.42 120.855     4 228.83 145.2134 <2e-16 ***
Habitat:Treatment   3.83   0.957     4 226.65   1.1497 0.3340    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to demonstrate that it doesn't add to the model.
predict(fullmodel1)
         1          4          5          6          7          8          9         10 
 0.9783831  2.1411337  1.3028518  1.8487338  1.6247945  1.9815684  1.5191378  1.4884911 
        11         13         14         15         16         18         19         21 
 0.9832969  1.7787921  2.1460476  1.3077657  1.8536476  1.9864822  1.5240516  0.9837652 
        22         23         24         25         26         27         28         29 
 2.2719098  1.7792604  2.1465159  1.3082340  1.8541159  1.6301767  1.9869505  1.5245199 
        30         31         32         33         35         37         38         40 
 1.4938733  0.9834082  2.2715527  1.7789034  1.3078770  1.6298196  1.9865935  1.4935163 
        41         42         44         46         47         48         49         51 
 0.9827552  2.2708997  2.1455059  1.8531059  1.6291666  1.9859405  1.5235099  0.9783656 
        52         53         55         56         57         58         59         60 
 2.2665102  1.7738608  1.3028344  1.8487163  1.6247771  1.9815509  1.5191203  1.4884737 
        62         63         64         66         67         68         69         71 
 2.2710672  1.7784179  2.1456733  1.8532734  1.6293341  1.9861080  1.5236774  0.9827714 
        72         73         76         77         78         81         83         85 
 2.2709159  1.7782665  1.8531221  1.6291828  1.9859567  0.9055791  1.7010743  1.2300479 
        86         87         88         89         90         93         94         95 
 1.7759298  1.5519905  1.9087644  1.4463338  1.4156872  1.7059881  2.0732436  1.2349617 
        96         97         98        100        102        103        105        106 
 1.7808436  1.5569043  1.9136782  1.4206010  2.1991058  1.7064564  1.2354300  1.7813120 
       107        109        111        112        113        114        116        118 
 1.5573727  1.4517160  0.9106042  2.1987488  1.7060994  2.0733549  1.7809549  1.9137895 
       119        120        122        123        124        127        128        129 
 1.4513589  1.4207123  2.1980958  1.7054464  2.0727019  1.5563627  1.9131366  1.4507059 
       130        131        133        134        135        136        137        138 
 1.4200593  0.9055616  1.7010568  2.0683123  1.2300304  1.7759124  1.5519731  1.9087470 
       139        140        141        142        143        144        147        148 
 1.4463164  1.4156697  0.9101187  2.1982633  1.7056139  2.0728694  1.5565301  1.9133040 
       149        151        153        154        155        156        157        158 
 1.4508734  0.9099674  1.7054626  2.0727181  1.2344362  1.7803181  1.5563788  1.9131527 
       159        160        161        163        164        170        172        174 
 1.4507221  1.4200755 -2.1889361 -1.3934409 -1.0261854 -1.6788280 -0.8958777 -1.0212716 
       184        185        189        195        196        201        210        211 
-1.0208033 -1.8590852 -1.6427992 -1.8594422 -1.3135603 -2.1845640 -1.6744559 -2.1889536 
       215        224        226        228        236        254        259        260 
-1.8644848 -1.0216458 -1.3140458 -1.1812112 -1.3141971 -0.4951009 -1.1170969 -1.1477435 
       270        272        280        281        284        285        286        287 
-1.1472751 -0.3695957 -1.1476322 -1.6583933 -0.4956426 -1.3339245 -0.7880426 -1.0119818 
       295        296        300        303        310        321        324        325 
-1.3383141 -0.7924321 -1.1526748 -0.8627306 -1.1481177 -2.2479869 -1.0852362 -1.9235181 
       326        327        334        351        353        354        361        367 
-1.3776362 -1.6015755 -1.0803224 -2.2429618 -1.4474666 -1.0802111 -2.2436148 -1.5972033 
       371        374        387        391        398        401        402        407 
-2.2480044 -1.0852537 -1.5970359 -2.2435986 -1.2404133 -2.0284798 -0.7403353 -1.3820684 
       413        414        421        424        437        441        446        461 
-1.2280708 -0.8608154 -2.0230977 -0.8603470 -1.3770433 -2.0241077 -1.1537570 -2.0239402 
       463        471        472        475        476        477        479        493 
-1.2284451 -2.0240915 -0.7359470 -1.6996228 -1.1537408 -1.3776801 -1.4833368 -1.1349432 
       495        498        499        500        527        548        569        570 
-1.6059696 -0.9272531 -1.3896837 -1.4203303 -1.2845687 -0.9276273 -1.0703998 -1.1010464 
       577        580        588        595        600        608        612        645 
-0.9598293 -1.0961326 -0.6025870 -1.2816606 -1.0960213 -0.6035971 -0.3230274 -1.3203070 
       653        659        660        661        665        667        675        677 
-0.8443668 -1.0991073 -1.1297539 -1.6393937 -1.3149249 -0.9929822 -1.3152819 -0.9933393 
       681        682        691        693        695        708        710        721 
-1.6404037 -0.3522591 -1.6447933 -0.8492981 -1.3203245 -0.6370509 -1.1301281 -1.9088882 
       727        731        734        735        738        740        751        752 
-1.2624768 -1.9039744 -0.7412237 -1.5795056 -0.9007890 -1.3938663 -1.9038631 -0.6157185 
       757        763        766        767        771        776        780        785 
-1.2574516 -1.1090209 -1.0341654 -1.2581046 -1.9089056 -1.0385549 -1.3987976 -1.5798798 
       789        791        794        795        796        798        800 
-1.3635939 -1.9044999 -0.7417492 -1.5800311 -1.0341492 -0.9013146 -1.3943918 
hist(predict(fullmodel1,type="response"))

##Model 2 - lmer: Modeling Site as a fixed effect

fullmodel2<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   Site+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + Site + (1 | Block)
   Data: reproduction_mydata

REML criterion at convergence: 678.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2839 -0.6210  0.0399  0.5639  2.7969 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.1650   0.4062  
 Residual             0.8361   0.9144  
Number of obs: 247, groups:  Block, 10

Fixed effects:
                                              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)                                  -1.720266   0.285577 105.107873  -6.024 2.54e-08
HabitatVegetated                              0.488869   0.318875 223.009734   1.533   0.1267
TreatmentBiomass Removal                      3.161011   0.244374 223.621849  12.935  < 2e-16
TreatmentHemizonia                           -0.004785   0.317523 223.398880  -0.015   0.9880
TreatmentRaking                               0.156859   0.426831 224.250434   0.367   0.7136
TreatmentRaking & Clipping                    0.509433   0.317070 223.835290   1.607   0.1095
SiteCHE                                       0.355322   0.232174 223.497740   1.530   0.1273
SiteGUA                                       0.410402   0.245081 221.592351   1.675   0.0954
SiteLEX                                       0.356617   0.233876 222.425736   1.525   0.1287
SiteOAP                                       0.316128   0.234233 222.925291   1.350   0.1785
SitePAR                                      -0.001442   0.231840 221.753218  -0.006   0.9950
SitePEN                                       0.347827   0.244024 223.399330   1.425   0.1554
SiteSSJ                                       0.315678   0.237514 222.147337   1.329   0.1852
HabitatVegetated:TreatmentBiomass Removal    -0.556028   0.358662 222.367673  -1.550   0.1225
HabitatVegetated:TreatmentHemizonia          -0.363654   0.452306 222.295756  -0.804   0.4223
HabitatVegetated:TreatmentRaking             -0.059139   0.573956 221.764455  -0.103   0.9180
HabitatVegetated:TreatmentRaking & Clipping  -0.744475   0.440856 223.170989  -1.689   0.0927
                                               
(Intercept)                                 ***
HabitatVegetated                               
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                                
TreatmentRaking & Clipping                     
SiteCHE                                        
SiteGUA                                     .  
SiteLEX                                        
SiteOAP                                        
SitePAR                                        
SitePEN                                        
SiteSSJ                                        
HabitatVegetated:TreatmentBiomass Removal      
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation matrix not shown by default, as p = 17 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it
anova(fullmodel2)
Type III Analysis of Variance Table with Satterthwaite's method
                  Sum Sq Mean Sq NumDF  DenDF  F value Pr(>F)    
Habitat             0.80   0.799     1 222.81   0.9554 0.3294    
Treatment         478.33 119.583     4 224.94 143.0199 <2e-16 ***
Site                5.74   0.820     7 222.78   0.9811 0.4458    
Habitat:Treatment   3.43   0.857     4 222.42   1.0244 0.3955    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Site as a fixed effect is not significant, therefore it should not be used as a fixed effect in addition to it not being used as a random effect.

##Model 3 - lmer: Site is removed from this model because it explains very little of the variance

fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Block)
   Data: reproduction_mydata

REML criterion at convergence: 675.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2235 -0.6825  0.0572  0.6111  2.9658 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.1839   0.4288  
 Residual             0.8326   0.9124  
Number of obs: 247, groups:  Block, 10

Fixed effects:
                                             Estimate Std. Error        df t value Pr(>|t|)
(Intercept)                                  -1.47141    0.25186  65.20283  -5.842 1.79e-07
HabitatVegetated                              0.52673    0.31273 229.69739   1.684   0.0935
TreatmentBiomass Removal                      3.16743    0.24299 230.30671  13.035  < 2e-16
TreatmentHemizonia                           -0.05973    0.31385 230.17407  -0.190   0.8492
TreatmentRaking                               0.25499    0.41045 230.89163   0.621   0.5351
TreatmentRaking & Clipping                    0.54463    0.31430 230.49078   1.733   0.0845
HabitatVegetated:TreatmentBiomass Removal    -0.59959    0.35315 229.14496  -1.698   0.0909
HabitatVegetated:TreatmentHemizonia          -0.30595    0.44325 229.12563  -0.690   0.4907
HabitatVegetated:TreatmentRaking             -0.20398    0.55605 228.65197  -0.367   0.7141
HabitatVegetated:TreatmentRaking & Clipping  -0.79088    0.43203 229.90026  -1.831   0.0685
                                               
(Intercept)                                 ***
HabitatVegetated                            .  
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                                
TreatmentRaking & Clipping                  .  
HabitatVegetated:TreatmentBiomass Removal   .  
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564                                                        
TrtmntBmssR -0.738  0.582                                                 
TretmntHmzn -0.558  0.439  0.582                                          
TretmntRkng -0.445  0.351  0.463  0.342                                   
TrtmntRkn&C -0.570  0.455  0.591  0.450  0.361                            
HbttVgt:TBR  0.500 -0.883 -0.675 -0.390 -0.309 -0.403                     
HbttVgtt:TH  0.392 -0.702 -0.405 -0.690 -0.246 -0.316  0.619              
HbttVgtt:TR  0.319 -0.555 -0.331 -0.252 -0.723 -0.258  0.490  0.391       
HbttVg:TR&C  0.413 -0.727 -0.425 -0.316 -0.262 -0.725  0.641  0.507  0.405
anova(fullmodel3)
Type III Analysis of Variance Table with Satterthwaite's method
                  Sum Sq Mean Sq NumDF  DenDF  F value Pr(>F)    
Habitat             0.84   0.844     1 229.78   1.0140 0.3150    
Treatment         483.49 120.873     4 231.54 145.1817 <2e-16 ***
Habitat:Treatment   3.83   0.959     4 229.30   1.1514 0.3332    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

###QQ Plots Now we’ll look at the QQ plots and the residuals using DHARMa

qqnorm(resid(fullmodel3)) #qqplot
qqline(resid(fullmodel3)) #add the line

testDispersion(fullmodel3) #red line should be in the middle of the distribution

    DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated

data:  simulationOutput
dispersion = 0.9764, p-value = 0.904
alternative hypothesis: two.sided

myDHARMagraph3<-simulateResiduals(fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph3) #plotting graph. At this point, you don't want any text or lines to be red.

When we compare the model summaries and Anova of Models 2 & 3, we see that the removal of Site doesn’t impact the model. Therefore the more simple Model 3 is a better choice. But let’s keep checking…

###Compare AIC Scores Now I need to compare the AIC scores for all the models to tell me which is the better model (but it will not say which fits my data better, that is why I did all the DHARMa stuff)

Using aictab to make the comparison of models and table

models<-list(fullmodel1,fullmodel2,fullmodel3)
mod.names<-c('Site.Random','Site.Fixed','No.Site')
aictab(cand.set=models,modnames=mod.names)
Warning: 
Model selection for fixed effects is only appropriate with ML estimation:
REML (default) should only be used to select random effects for a constant set of fixed effects

Model selection based on AICc:

             K   AICc Delta_AICc AICcWt Cum.Wt  Res.LL
No.Site     12 700.60       0.00   0.75   0.75 -337.63
Site.Random 13 702.82       2.23   0.25   1.00 -337.63
Site.Fixed  19 719.64      19.04   0.00   1.00 -339.15
#The lowest AICc score is listed first and indicates the best fitting model, here, Model 3 (No.Site) where Site is not included. The cut-off for comparing Models is 2 units. The difference between Model 1 (Site.Random) and Model 3 (No.Site) is 2.12. So Model 3 is marginally better than Model 2.

##Other Models Attempted Other ideas that were explored to keep Site but resulted in singular error and Site explaining 0.000 of the variance: fullmodel4<-lmer(log(Biomass)~ Treatment + (1|Site) + (1|Block), data = mydata) summary(fullmodel4) fullmodel5<-lmer(log(Biomass)~ Treatment + Habitat + (1|Site) + (1|Block), data = mydata) summary(fullmodel5)

###Best Model

fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Habitat * Treatment + (1 | Block)
   Data: reproduction_mydata

REML criterion at convergence: 675.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2235 -0.6825  0.0572  0.6111  2.9658 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.1839   0.4288  
 Residual             0.8326   0.9124  
Number of obs: 247, groups:  Block, 10

Fixed effects:
                                             Estimate Std. Error        df t value Pr(>|t|)
(Intercept)                                  -1.47141    0.25186  65.20283  -5.842 1.79e-07
HabitatVegetated                              0.52673    0.31273 229.69739   1.684   0.0935
TreatmentBiomass Removal                      3.16743    0.24299 230.30671  13.035  < 2e-16
TreatmentHemizonia                           -0.05973    0.31385 230.17407  -0.190   0.8492
TreatmentRaking                               0.25499    0.41045 230.89163   0.621   0.5351
TreatmentRaking & Clipping                    0.54463    0.31430 230.49078   1.733   0.0845
HabitatVegetated:TreatmentBiomass Removal    -0.59959    0.35315 229.14496  -1.698   0.0909
HabitatVegetated:TreatmentHemizonia          -0.30595    0.44325 229.12563  -0.690   0.4907
HabitatVegetated:TreatmentRaking             -0.20398    0.55605 228.65197  -0.367   0.7141
HabitatVegetated:TreatmentRaking & Clipping  -0.79088    0.43203 229.90026  -1.831   0.0685
                                               
(Intercept)                                 ***
HabitatVegetated                            .  
TreatmentBiomass Removal                    ***
TreatmentHemizonia                             
TreatmentRaking                                
TreatmentRaking & Clipping                  .  
HabitatVegetated:TreatmentBiomass Removal   .  
HabitatVegetated:TreatmentHemizonia            
HabitatVegetated:TreatmentRaking               
HabitatVegetated:TreatmentRaking & Clipping .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) HbttVg TrtmBR TrtmnH TrtmnR TrtR&C HV:TBR HbV:TH HbV:TR
HabittVgttd -0.564                                                        
TrtmntBmssR -0.738  0.582                                                 
TretmntHmzn -0.558  0.439  0.582                                          
TretmntRkng -0.445  0.351  0.463  0.342                                   
TrtmntRkn&C -0.570  0.455  0.591  0.450  0.361                            
HbttVgt:TBR  0.500 -0.883 -0.675 -0.390 -0.309 -0.403                     
HbttVgtt:TH  0.392 -0.702 -0.405 -0.690 -0.246 -0.316  0.619              
HbttVgtt:TR  0.319 -0.555 -0.331 -0.252 -0.723 -0.258  0.490  0.391       
HbttVg:TR&C  0.413 -0.727 -0.425 -0.316 -0.262 -0.725  0.641  0.507  0.405

###Post-Hoc Test You should remove non-significant interaction terms before running a post-hoc test. It is difficult to judge the main effects (Habitat and Site) when you also have the interaction term present, so when it is not significant, fit a new model without it.

fullmodel3.1<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block),
                   data=reproduction_mydata)
summary(fullmodel3.1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Treatment + (1 | Block)
   Data: reproduction_mydata

REML criterion at convergence: 677

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1914 -0.6482  0.0311  0.6048  3.1267 

Random effects:
 Groups   Name        Variance Std.Dev.
 Block    (Intercept) 0.1819   0.4265  
 Residual             0.8320   0.9121  
Number of obs: 247, groups:  Block, 10

Fixed effects:
                           Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)                 -1.2310     0.2074  33.6195  -5.936 1.09e-06 ***
TreatmentBiomass Removal     2.8917     0.1790 236.6910  16.159  < 2e-16 ***
TreatmentHemizonia          -0.1744     0.2261 237.1867  -0.772    0.441    
TreatmentRaking              0.1873     0.2822 237.0309   0.664    0.508    
TreatmentRaking & Clipping   0.1447     0.2140 235.2223   0.676    0.500    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) TrtmBR TrtmnH TrtmnR
TrtmntBmssR -0.673                     
TretmntHmzn -0.527  0.616              
TretmntRkng -0.428  0.500  0.368       
TrtmntRkn&C -0.553  0.644  0.516  0.412
anova(fullmodel3.1)
Type III Analysis of Variance Table with Satterthwaite's method
          Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Treatment 490.29  122.57     4 236.51  147.33 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
qqnorm(resid(fullmodel3.1)) #qqplot
qqline(resid(fullmodel3.1)) #add the line

testDispersion(fullmodel3.1) #red line should be in the middle of the distribution

    DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated

data:  simulationOutput
dispersion = 0.99335, p-value = 0.896
alternative hypothesis: two.sided

myDHARMagraph3.1<-simulateResiduals(fullmodel3.1) #testing for heteroscedasticity
plot(myDHARMagraph3.1) #plotting graph

fullmodel3.2<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block)+
                     (1|Population),
                   data=mydata)
summary(fullmodel3.2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Biomass) ~ Treatment + (1 | Block) + (1 | Population)
   Data: mydata

REML criterion at convergence: 1381.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.4116 -0.6367 -0.0001  0.6389  3.6745 

Random effects:
 Groups     Name        Variance Std.Dev.
 Population (Intercept) 0.000801 0.0283  
 Block      (Intercept) 0.060607 0.2462  
 Residual               1.290867 1.1362  
Number of obs: 441, groups:  Population, 16; Block, 10

Fixed effects:
                           Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)                 -2.0644     0.1565  52.0231 -13.195   <2e-16 ***
TreatmentBiomass Removal     3.5659     0.1650 426.7907  21.609   <2e-16 ***
TreatmentHemizonia          -0.0984     0.1952 432.3439  -0.504   0.6145    
TreatmentRaking             -0.2757     0.1920 428.3744  -1.436   0.1517    
TreatmentRaking & Clipping   0.3912     0.1822 428.2538   2.147   0.0324 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) TrtmBR TrtmnH TrtmnR
TrtmntBmssR -0.712                     
TretmntHmzn -0.602  0.571              
TretmntRkng -0.608  0.577  0.484       
TrtmntRkn&C -0.641  0.607  0.519  0.519
anova(fullmodel3.2)
Type III Analysis of Variance Table with Satterthwaite's method
          Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Treatment   1233  308.25     4 426.32  238.79 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
qqnorm(resid(fullmodel3.2)) #qqplot
qqline(resid(fullmodel3.2)) #add the line

testDispersion(fullmodel3.2) #red line should be in the middle of the distribution

    DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated

data:  simulationOutput
dispersion = 0.98522, p-value = 0.904
alternative hypothesis: two.sided

myDHARMagraph3.2<-simulateResiduals(fullmodel3.2) #testing for heteroscedasticity
plot(myDHARMagraph3.2) #plotting graph

Let’s use emmeans for our Best Model (fullmodel3) and the two off-shoots.

#?emmeans, emmeans(model, pairwise ~ treatment)
emmeans(fullmodel3,pairwise~Treatment)
NOTE: Results may be misleading due to involvement in interactions
$emmeans
 Treatment         emmean    SE   df lower.CL upper.CL
 Grassland          -1.21 0.209 33.3    -1.63   -0.784
 Biomass Removal     1.66 0.159 11.9     1.31    2.006
 Hemizonia          -1.42 0.213 35.1    -1.85   -0.989
 Raking             -1.06 0.272 80.4    -1.60   -0.514
 Raking & Clipping  -1.06 0.202 29.9    -1.47   -0.646

Results are averaged over the levels of: Habitat 
Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$contrasts
 contrast                            estimate    SE  df t.ratio p.value
 Grassland - Biomass Removal          -2.8676 0.180 232 -15.931  <.0001
 Grassland - Hemizonia                 0.2127 0.228 232   0.934  0.8833
 Grassland - Raking                   -0.1530 0.285 232  -0.537  0.9834
 Grassland - Raking & Clipping        -0.1492 0.217 230  -0.687  0.9592
 Biomass Removal - Hemizonia           3.0803 0.183 233  16.789  <.0001
 Biomass Removal - Raking              2.7146 0.249 231  10.886  <.0001
 Biomass Removal - Raking & Clipping   2.7184 0.172 231  15.811  <.0001
 Hemizonia - Raking                   -0.3657 0.292 234  -1.251  0.7211
 Hemizonia - Raking & Clipping        -0.3619 0.220 231  -1.645  0.4700
 Raking - Raking & Clipping            0.0038 0.279 231   0.014  1.0000

Results are averaged over the levels of: Habitat 
Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 5 estimates 
emmeans(fullmodel3.1,pairwise~Treatment)
$emmeans
 Treatment         emmean    SE   df lower.CL upper.CL
 Grassland          -1.23 0.208 33.4    -1.65   -0.809
 Biomass Removal     1.66 0.158 12.0     1.32    2.006
 Hemizonia          -1.41 0.212 35.4    -1.84   -0.976
 Raking             -1.04 0.270 80.2    -1.58   -0.507
 Raking & Clipping  -1.09 0.199 29.0    -1.49   -0.678

Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$contrasts
 contrast                            estimate    SE  df t.ratio p.value
 Grassland - Biomass Removal          -2.8917 0.179 237 -16.126  <.0001
 Grassland - Hemizonia                 0.1744 0.227 237   0.770  0.9391
 Grassland - Raking                   -0.1873 0.283 237  -0.662  0.9642
 Grassland - Raking & Clipping        -0.1447 0.214 235  -0.675  0.9616
 Biomass Removal - Hemizonia           3.0661 0.183 238  16.756  <.0001
 Biomass Removal - Raking              2.7044 0.248 236  10.916  <.0001
 Biomass Removal - Raking & Clipping   2.7469 0.169 236  16.249  <.0001
 Hemizonia - Raking                   -0.3617 0.291 239  -1.245  0.7249
 Hemizonia - Raking & Clipping        -0.3192 0.217 236  -1.470  0.5829
 Raking - Raking & Clipping            0.0425 0.276 236   0.154  0.9999

Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 5 estimates 
emmeans(fullmodel3.2,pairwise~Treatment)
$emmeans
 Treatment         emmean    SE   df lower.CL upper.CL
 Grassland          -2.06 0.157 57.5    -2.38    -1.75
 Biomass Removal     1.50 0.122 23.1     1.25     1.75
 Hemizonia          -2.16 0.161 62.1    -2.49    -1.84
 Raking             -2.34 0.158 58.9    -2.66    -2.02
 Raking & Clipping  -1.67 0.146 44.0    -1.97    -1.38

Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$contrasts
 contrast                            estimate    SE  df t.ratio p.value
 Grassland - Biomass Removal          -3.5659 0.166 428 -21.525  <.0001
 Grassland - Hemizonia                 0.0984 0.196 433   0.501  0.9873
 Grassland - Raking                    0.2757 0.193 429   1.429  0.6092
 Grassland - Raking & Clipping        -0.3912 0.183 429  -2.136  0.2067
 Biomass Removal - Hemizonia           3.6643 0.170 428  21.602  <.0001
 Biomass Removal - Raking              3.8416 0.166 426  23.076  <.0001
 Biomass Removal - Raking & Clipping   3.1746 0.155 423  20.486  <.0001
 Hemizonia - Raking                    0.1773 0.198 433   0.896  0.8985
 Hemizonia - Raking & Clipping        -0.4896 0.186 427  -2.631  0.0666
 Raking - Raking & Clipping           -0.6669 0.184 423  -3.620  0.0030

Degrees-of-freedom method: kenward-roger 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 5 estimates 
#These models result in similar Tukey outcomes

##ggplot - lmer

ggplot(data=reproduction_mydata,
       aes(x=Treatment,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)

ggplot(data=reproduction_mydata,
       aes(x=Habitat,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)

#Survival Analysis This code uses NumDaysAlive data with Habitat (roadside and vegetated) and Treatment in a Cox proportional hazards model to assess survival. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. “Surv” creates a survival object to combine the days column (NumDaysAlive) and the reproductive censor column (ReproductionCensor) to be used as a response variable in a model formula. The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: “var1 * var2” will give you the interaction term and the individual variables: so “var1 + var2 + var1:var2”. To add random effects, type “+ (1|random effect)”.

##Histograms

#Number of Days Alive - All data
hist(mydata$NumDaysAlive,col='steelblue',main='Original') 


#Number of Days Alive - By Habitat
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Habitat)) #Here we see that both Habitats have the same bi-modal distribution.


#Number of Days Alive - By Treatment
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that 4 of the treatments have the same bi-modal distribution and Biomass Removal is right skewed.

##Cox Model Start by making a simple model with no random effects. This will be compared to the full model with random effects.

cox_simplemodel1<-coxph(Surv(NumDaysAlive,
                             CensorReproduction)~
                          Habitat*Treatment,
                        data=mydata)
summary(cox_simplemodel1)
Call:
coxph(formula = Surv(NumDaysAlive, CensorReproduction) ~ Habitat * 
    Treatment, data = mydata)

  n= 800, number of events= 253 

                                               coef exp(coef) se(coef)      z Pr(>|z|)    
HabitatVegetated                            -0.5980    0.5499   0.3308 -1.808   0.0706 .  
TreatmentBiomass Removal                     1.2401    3.4559   0.2771  4.475 7.65e-06 ***
TreatmentHemizonia                           0.1563    1.1692   0.3364  0.465   0.6422    
TreatmentRaking                             -0.8104    0.4447   0.4404 -1.840   0.0657 .  
TreatmentRaking & Clipping                   0.3718    1.4504   0.3304  1.125   0.2604    
HabitatVegetated:TreatmentBiomass Removal    0.5848    1.7946   0.3772  1.550   0.1210    
HabitatVegetated:TreatmentHemizonia          0.2799    1.3229   0.4740  0.590   0.5549    
HabitatVegetated:TreatmentRaking             1.1347    3.1102   0.5861  1.936   0.0529 .  
HabitatVegetated:TreatmentRaking & Clipping  0.8470    2.3327   0.4581  1.849   0.0645 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                            exp(coef) exp(-coef) lower .95 upper .95
HabitatVegetated                               0.5499     1.8185    0.2875     1.052
TreatmentBiomass Removal                       3.4559     0.2894    2.0076     5.949
TreatmentHemizonia                             1.1692     0.8553    0.6047     2.261
TreatmentRaking                                0.4447     2.2489    0.1876     1.054
TreatmentRaking & Clipping                     1.4504     0.6895    0.7590     2.771
HabitatVegetated:TreatmentBiomass Removal      1.7946     0.5572    0.8569     3.758
HabitatVegetated:TreatmentHemizonia            1.3229     0.7559    0.5224     3.350
HabitatVegetated:TreatmentRaking               3.1102     0.3215    0.9861     9.809
HabitatVegetated:TreatmentRaking & Clipping    2.3327     0.4287    0.9504     5.725

Concordance= 0.604  (se = 0.024 )
Likelihood ratio test= 90.68  on 9 df,   p=1e-15
Wald test            = 77.28  on 9 df,   p=6e-13
Score (logrank) test = 87.13  on 9 df,   p=6e-15
print(cox_simplemodel1)
Call:
coxph(formula = Surv(NumDaysAlive, CensorReproduction) ~ Habitat * 
    Treatment, data = mydata)

                                               coef exp(coef) se(coef)      z        p
HabitatVegetated                            -0.5980    0.5499   0.3308 -1.808   0.0706
TreatmentBiomass Removal                     1.2401    3.4559   0.2771  4.475 7.65e-06
TreatmentHemizonia                           0.1563    1.1692   0.3364  0.465   0.6422
TreatmentRaking                             -0.8104    0.4447   0.4404 -1.840   0.0657
TreatmentRaking & Clipping                   0.3718    1.4504   0.3304  1.125   0.2604
HabitatVegetated:TreatmentBiomass Removal    0.5848    1.7946   0.3772  1.550   0.1210
HabitatVegetated:TreatmentHemizonia          0.2799    1.3229   0.4740  0.590   0.5549
HabitatVegetated:TreatmentRaking             1.1347    3.1102   0.5861  1.936   0.0529
HabitatVegetated:TreatmentRaking & Clipping  0.8470    2.3327   0.4581  1.849   0.0645

Likelihood ratio test=90.68  on 9 df, p=1.187e-15
n= 800, number of events= 253 
predict(cox_simplemodel1)
  [1]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
  [9]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [17]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [25]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [33]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [41]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [49]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [57]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [65]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [73]  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817  1.2400817
 [81]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
 [89]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
 [97]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[105]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[113]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[121]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[129]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[137]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[145]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[153]  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298  1.2268298
[161]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[169]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[177]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[185]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[193]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[201]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[209]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[217]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[225]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[233]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[241] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[249] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[257] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[265] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[273] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[281] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[289] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[297] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[305] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[313] -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207 -0.5980207
[321]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[329]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[337]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[345]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[353]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[361]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[369]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[377]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[385]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[393]  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201  0.1563201
[401] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[409] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[417] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[425] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[433] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[441] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[449] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[457] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[465] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[473] -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398 -0.1618398
[481] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[489] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[497] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[505] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[513] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[521] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[529] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[537] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[545] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[553] -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351 -0.8104351
[561] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[569] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[577] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[585] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[593] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[601] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[609] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[617] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[625] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[633] -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686 -0.2737686
[641]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[649]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[657]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[665]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[673]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[681]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[689]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[697]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[705]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[713]  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368  0.3718368
[721]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[729]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[737]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[745]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[753]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[761]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[769]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[777]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[785]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
[793]  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270  0.6208270
hist(predict(cox_simplemodel1))

Now make a full model using random effects

cox_fullmodel1<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat*Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata)
summary(cox_fullmodel1)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 19 119 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1117.392 -1093.286

                   Chisq    df p    AIC    BIC
Integrated loglik 173.68 11.00 0 151.68 112.81
 Penalized loglik 221.89 22.42 0 177.04  97.81

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat * Treatment +      (1 | Site) + (1 | Block) 
Fixed coefficients
                                                   coef exp(coef)  se(coef)     z       p
HabitatVegetated                            -0.37919954 0.6844090 0.3444499 -1.10 2.7e-01
TreatmentBiomass Removal                     1.38647395 4.0007184 0.2911902  4.76 1.9e-06
TreatmentHemizonia                          -0.09751812 0.9070859 0.3540413 -0.28 7.8e-01
TreatmentRaking                             -0.64329505 0.5255578 0.4572637 -1.41 1.6e-01
TreatmentRaking & Clipping                   0.59622662 1.8152562 0.3498383  1.70 8.8e-02
HabitatVegetated:TreatmentBiomass Removal    0.45521372 1.5765103 0.3874747  1.17 2.4e-01
HabitatVegetated:TreatmentHemizonia          0.25268759 1.2874810 0.4903683  0.52 6.1e-01
HabitatVegetated:TreatmentRaking             0.80143838 2.2287444 0.6000419  1.34 1.8e-01
HabitatVegetated:TreatmentRaking & Clipping  0.68420207 1.9821896 0.4766792  1.44 1.5e-01

Random effects
 Group Variable  Std Dev    Variance  
 Site  Intercept 0.29585965 0.08753293
 Block Intercept 0.83465076 0.69664189
print(cox_fullmodel1)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 19 119 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1117.392 -1093.286

                   Chisq    df p    AIC    BIC
Integrated loglik 173.68 11.00 0 151.68 112.81
 Penalized loglik 221.89 22.42 0 177.04  97.81

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat * Treatment +      (1 | Site) + (1 | Block) 
Fixed coefficients
                                                   coef exp(coef)  se(coef)     z       p
HabitatVegetated                            -0.37919954 0.6844090 0.3444499 -1.10 2.7e-01
TreatmentBiomass Removal                     1.38647395 4.0007184 0.2911902  4.76 1.9e-06
TreatmentHemizonia                          -0.09751812 0.9070859 0.3540413 -0.28 7.8e-01
TreatmentRaking                             -0.64329505 0.5255578 0.4572637 -1.41 1.6e-01
TreatmentRaking & Clipping                   0.59622662 1.8152562 0.3498383  1.70 8.8e-02
HabitatVegetated:TreatmentBiomass Removal    0.45521372 1.5765103 0.3874747  1.17 2.4e-01
HabitatVegetated:TreatmentHemizonia          0.25268759 1.2874810 0.4903683  0.52 6.1e-01
HabitatVegetated:TreatmentRaking             0.80143838 2.2287444 0.6000419  1.34 1.8e-01
HabitatVegetated:TreatmentRaking & Clipping  0.68420207 1.9821896 0.4766792  1.44 1.5e-01

Random effects
 Group Variable  Std Dev    Variance  
 Site  Intercept 0.29585965 0.08753293
 Block Intercept 0.83465076 0.69664189
predict(cox_fullmodel1)
  [1]  3.412069248  1.723904182  1.142354944  1.411114662  2.826926974  1.710423900
  [7]  1.521797855  1.543482099  1.020912356  0.832419259  3.057516702  1.369351636
 [13]  0.787802397  1.056562116  2.472374428  1.355871354  1.167245309  1.188929553
 [19]  0.666359810  0.477866713  3.027300084  1.339135018  0.757585779  1.026345498
 [25]  2.442157809  1.325654736  1.137028691  1.158712935  0.636143192  0.447650095
 [31]  3.111607266  1.423442200  0.841892961  1.110652680  2.526464992  1.409961918
 [37]  1.221335873  1.243020117  0.720450374  0.531957277  2.783497956  1.095332890
 [43]  0.513783652  0.782543370  2.198355682  1.081852608  0.893226563  0.914910807
 [49]  0.392341064  0.203847967  3.491439676  1.803274610  1.221725371  1.490485090
 [55]  2.906297402  1.789794328  1.601168283  1.622852527  1.100282784  0.911789687
 [61]  2.874174708  1.186009641  0.604460403  0.873220121  2.289032433  1.172529359
 [67]  0.983903315  1.005587559  0.483017815  0.294524719  2.914415579  1.226250512
 [73]  0.644701274  0.913460992  2.329273304  1.212770231  1.024144186  1.045828430
 [79]  0.523258686  0.334765590  3.488083434  1.799918367  1.218369129  1.487128847
 [85]  2.902941159  1.786438086  1.597812041  1.619496285  1.096926541  0.908433445
 [91]  3.133530888  1.445365821  0.863816583  1.132576301  2.548388613  1.431885540
 [97]  1.243259495  1.264943739  0.742373995  0.553880899  3.103314270  1.415149203
[103]  0.833599965  1.102359683  2.518171995  1.401668921  1.213042877  1.234727121
[109]  0.712157377  0.523664281  3.187621452  1.499456385  0.917907147  1.186666865
[115]  2.602479177  1.485976104  1.297350059  1.319034303  0.796464559  0.607971463
[121]  2.859512142  1.171347075  0.589797837  0.858557555  2.274369867  1.157866794
[127]  0.969240749  0.990924993  0.468355249  0.279862153  3.567453862  1.879288795
[133]  1.297739557  1.566499275  2.982311587  1.865808513  1.677182469  1.698866713
[139]  1.176296969  0.987803873  2.950188893  1.262023827  0.680474588  0.949234307
[145]  2.365046619  1.248543545  1.059917500  1.081601744  0.559032001  0.370538904
[151]  2.990429765  1.302264698  0.720715460  0.989475178  2.405287490  1.288784416
[157]  1.100158372  1.121842616  0.599272872  0.410779776  2.025595296  0.337430230
[163] -0.244119009  0.024640710  1.440453022  0.323949948  0.135323903  0.157008147
[169] -0.365561596 -0.554054693  1.671042750 -0.017122316 -0.598671555 -0.329911836
[175]  1.085900476 -0.030602598 -0.219228643 -0.197544399 -0.720114142 -0.908607239
[181]  1.640826132 -0.047338935 -0.628888173 -0.360128455  1.055683857 -0.060819216
[187] -0.249445261 -0.227761017 -0.750330761 -0.938823857  1.725133314  0.036968248
[193] -0.544580991 -0.275821272  1.139991040  0.023487966 -0.165138079 -0.143453835
[199] -0.666023578 -0.854516675  1.397024004 -0.291141062 -0.872690301 -0.603930582
[205]  0.811881730 -0.304621344 -0.493247389 -0.471563145 -0.994132888 -1.182625985
[211]  2.104965724  0.416800657 -0.164748581  0.104011137  1.519823449  0.403320376
[217]  0.214694331  0.236378575 -0.286191169 -0.474684265  1.487700756 -0.200464311
[223] -0.782013549 -0.513253831  0.902558481 -0.213944593 -0.402570638 -0.380886393
[229] -0.903456137 -1.091949233  1.527941627 -0.160223440 -0.741772678 -0.473012960
[235]  0.942799352 -0.173703721 -0.362329766 -0.340645522 -0.863215266 -1.051708362
[241]  1.646395758 -0.041769308 -0.623318547 -0.354558828  1.061253484 -0.055249590
[247] -0.243875635 -0.222191391 -0.744761134 -0.933254231  1.291843212 -0.396321855
[253] -0.977871093 -0.709111374  0.706700937 -0.409802136 -0.598428181 -0.576743937
[259] -1.099313681 -1.287806777  1.261626594 -0.426538473 -1.008087711 -0.739327993
[265]  0.676484319 -0.440018754 -0.628644799 -0.606960555 -1.129530299 -1.318023395
[271]  1.345933776 -0.342231290 -0.923780529 -0.655020810  0.760791502 -0.355711572
[277] -0.544337617 -0.522653373 -1.045223116 -1.233716213  1.017824466 -0.670340600
[283] -1.251889839 -0.983130120  0.432682192 -0.683820882 -0.872446927 -0.850762683
[289] -1.373332426 -1.561825523  1.725766186  0.037601119 -0.543948119 -0.275188401
[295]  1.140623911  0.024120838 -0.164505207 -0.142820963 -0.665390707 -0.853883803
[301]  1.108501217 -0.579663849 -1.161213087 -0.892453369  0.523358943 -0.593144131
[307] -0.781770176 -0.760085932 -1.282655675 -1.471148772  1.148742089 -0.539422978
[313] -1.120972216 -0.852212498  0.563599814 -0.552903260 -0.741529304 -0.719845060
[319] -1.242414804 -1.430907900  1.928077178  0.239912111 -0.341637127 -0.072877409
[325]  1.342934903  0.226431829  0.037805785  0.059490029 -0.463079715 -0.651572811
[331]  1.573524632 -0.114640435 -0.696189673 -0.427429955  0.988382357 -0.128120717
[337] -0.316746761 -0.295062517 -0.817632261 -1.006125357  1.543308014 -0.144857053
[343] -0.726406291 -0.457646573  0.958165739 -0.158337335 -0.346963380 -0.325279135
[349] -0.847848879 -1.036341975  1.627615196 -0.060549871 -0.642099109 -0.373339391
[355]  1.042472921 -0.074030153 -0.262656197 -0.240971953 -0.763541697 -0.952034793
[361]  1.299505886 -0.388659181 -0.970208419 -0.701448701  0.714363611 -0.402139463
[367] -0.590765507 -0.569081263 -1.091651007 -1.280144103  2.007447606  0.319282539
[373] -0.262266699  0.006493019  1.422305331  0.305802257  0.117176212  0.138860457
[379] -0.383709287 -0.572202383  1.390182637 -0.297982430 -0.879531668 -0.610771950
[385]  0.805040362 -0.311462711 -0.500088756 -0.478404512 -1.000974256 -1.189467352
[391]  1.430423508 -0.257741558 -0.839290797 -0.570531078  0.845281234 -0.271221840
[397] -0.459847885 -0.438163641 -0.960733384 -1.149226481  1.801565225  0.113400159
[403] -0.468149079 -0.199389361  1.216422951  0.099919877 -0.088706168 -0.067021924
[409] -0.589591667 -0.778084764  1.447012679 -0.241152387 -0.822701626 -0.553941907
[415]  0.861870405 -0.254632669 -0.443258714 -0.421574470 -0.944144213 -1.132637310
[421]  1.416796061 -0.271369005 -0.852918244 -0.584158525  0.831653787 -0.284849287
[427] -0.473475332 -0.451791088 -0.974360831 -1.162853928  1.501103243 -0.187061823
[433] -0.768611061 -0.499851343  0.915960969 -0.200542105 -0.389168150 -0.367483906
[439] -0.890053649 -1.078546746  1.172993933 -0.515171133 -1.096720371 -0.827960653
[445]  0.587851659 -0.528651415 -0.717277460 -0.695593216 -1.218162959 -1.406656056
[451]  1.880935653  0.192770587 -0.388778652 -0.120018933  1.295793379  0.179290305
[457] -0.009335740  0.012348504 -0.510221239 -0.698714336  1.263670685 -0.424494382
[463] -1.006043620 -0.737283902  0.678528410 -0.437974664 -0.626600708 -0.604916464
[469] -1.127486208 -1.315979304  1.303911556 -0.384253511 -0.965802749 -0.697043031
[475]  0.718769281 -0.397733792 -0.586359837 -0.564675593 -1.087245337 -1.275738433
[481]  1.382300247 -0.305864820 -0.887414058 -0.618654340  0.797157972 -0.319345102
[487] -0.507971146 -0.486286902 -1.008856646 -1.197349742  1.027747700 -0.660417366
[493] -1.241966604 -0.973206886  0.442605426 -0.673897648 -0.862523693 -0.840839449
[499] -1.363409192 -1.551902288  0.997531082 -0.690633984 -1.272183223 -1.003423504
[505]  0.412388808 -0.704114266 -0.892740311 -0.871056067 -1.393625810 -1.582118907
[511]  1.081838265 -0.606326802 -1.187876040 -0.919116322  0.496695990 -0.619807084
[517] -0.808433128 -0.786748884 -1.309318628 -1.497811724  0.753728955 -0.934436112
[523] -1.515985350 -1.247225632  0.168586680 -0.947916394 -1.136542438 -1.114858194
[529] -1.637427938 -1.825921034  1.461670674 -0.226494392 -0.808043631 -0.539283912
[535]  0.876528400 -0.239974674 -0.428600719 -0.406916475 -0.929486218 -1.117979315
[541]  0.844405706 -0.843759361 -1.425308599 -1.156548881  0.259263431 -0.857239643
[547] -1.045865687 -1.024181443 -1.546751187 -1.735244283  0.884646577 -0.803518489
[553] -1.385067728 -1.116308009  0.299504303 -0.816998771 -1.005624816 -0.983940572
[559] -1.506510315 -1.695003412  1.804539087  0.116374020 -0.465175218 -0.196415500
[565]  1.219396812  0.102893738 -0.085732307 -0.064048062 -0.586617806 -0.775110902
[571]  1.449986540 -0.238178526 -0.819727765 -0.550968046  0.864844266 -0.251658808
[577] -0.440284853 -0.418600609 -0.941170352 -1.129663449  1.419769922 -0.268395144
[583] -0.849944383 -0.581184664  0.834627648 -0.281875426 -0.470501471 -0.448817227
[589] -0.971386970 -1.159880067  1.504077104 -0.184087962 -0.765637200 -0.496877482
[595]  0.918934830 -0.197568244 -0.386194289 -0.364510045 -0.887079788 -1.075572884
[601]  1.175967795 -0.512197272 -1.093746510 -0.824986792  0.590825520 -0.525677554
[607] -0.714303599 -0.692619355 -1.215189098 -1.403682194  1.883909514  0.195744448
[613] -0.385804791 -0.117045072  1.298767240  0.182264166 -0.006361879  0.015322365
[619] -0.507247378 -0.695740475  1.266644546 -0.421520521 -1.003069759 -0.734310041
[625]  0.681502271 -0.435000803 -0.623626847 -0.601942603 -1.124512347 -1.313005443
[631]  1.306885417 -0.381279650 -0.962828888 -0.694069170  0.721743142 -0.394759931
[637] -0.583385976 -0.561701732 -1.084271476 -1.272764572  2.621821912  0.933656845
[643]  0.352107607  0.620867325  2.036679637  0.920176564  0.731550519  0.753234763
[649]  0.230665019  0.042171923  2.267269366  0.579104299 -0.002444939  0.266314779
[655]  1.682127091  0.565624018  0.376997973  0.398682217 -0.123887527 -0.312380623
[661]  2.237052748  0.548887681 -0.032661557  0.236098161  1.651910473  0.535407399
[667]  0.346781355  0.368465599 -0.154104145 -0.342597241  2.321359930  0.633194863
[673]  0.051645625  0.320405343  1.736217655  0.619714582  0.431088537  0.452772781
[679] -0.069796963 -0.258290059  1.993250620  0.305085553 -0.276463685 -0.007703967
[685]  1.408108345  0.291605272  0.102979227  0.124663471 -0.397906273 -0.586399369
[691]  2.701192340  1.013027273  0.431478035  0.700237753  2.116050065  0.999546991
[697]  0.810920947  0.832605191  0.310035447  0.121542351  2.083927371  0.395762305
[703] -0.185786934  0.082972785  1.498785097  0.382282023  0.193655978  0.215340222
[709] -0.307229521 -0.495722618  2.124168243  0.436003176 -0.145546062  0.123213656
[715]  1.539025968  0.422522894  0.233896850  0.255581094 -0.266988650 -0.455481746
[721]  2.926824446  1.238659380  0.657110141  0.925869860  2.341682172  1.225179098
[727]  1.036553053  1.058237297  0.535667554  0.347174457  2.572271900  0.884106833
[733]  0.302557595  0.571317313  1.987129625  0.870626552  0.682000507  0.703684751
[739]  0.181115007 -0.007378089  2.542055282  0.853890215  0.272340977  0.541100695
[745]  1.956913007  0.840409934  0.651783889  0.673468133  0.150898389 -0.037594707
[751]  2.626362464  0.938197398  0.356648159  0.625407878  2.041220189  0.924717116
[757]  0.736091071  0.757775315  0.235205572  0.046712475  2.298253154  0.610088088
[763]  0.028538849  0.297298568  1.713110880  0.596607806  0.407981761  0.429666005
[769] -0.092903738 -0.281396835  3.006194874  1.318029807  0.736480569  1.005240287
[775]  2.421052599  1.304549526  1.115923481  1.137607725  0.615037981  0.426544885
[781]  2.388929905  0.700764839  0.119215600  0.387975319  1.803787631  0.687284557
[787]  0.498658512  0.520342756 -0.002226987 -0.190720084  2.429170777  0.741005710
[793]  0.159456472  0.428216190  1.844028502  0.727525428  0.538899384  0.560583628
[799]  0.038013884 -0.150479212
hist(predict(cox_fullmodel1))

Now we can compare the models to see which model is best

anova(cox_simplemodel1,cox_fullmodel1) 
Analysis of Deviance Table
 Cox model: response is  Surv(NumDaysAlive, CensorReproduction)
 Model 1: ~Habitat * Treatment
 Model 2: ~Habitat * Treatment + (1 | Site) + (1 | Block)
   loglik  Chisq Df P(>|Chi|)    
1 -1158.9                        
2 -1117.4 82.993  2 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#See example: https://www.rdocumentation.org/packages/coxme/versions/2.2-16/topics/coxme
AIC(cox_simplemodel1,cox_fullmodel1) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the fullmodel1.

Now, let’s make a model with no interaction term

cox_fullmodel2<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel2)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 18 95 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1118.851 -1094.381

                   Chisq    df p    AIC    BIC
Integrated loglik 170.76  7.00 0 156.76 132.02
 Penalized loglik 219.70 18.58 0 182.53 116.86

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment +      (1 | Site) + (1 | Block) 
Fixed coefficients
                                  coef exp(coef)  se(coef)     z       p
HabitatVegetated            0.04968034 1.0509351 0.1326079  0.37 7.1e-01
TreatmentBiomass Removal    1.60910167 4.9983191 0.2268731  7.09 1.3e-12
TreatmentHemizonia          0.01272746 1.0128088 0.2465085  0.05 9.6e-01
TreatmentRaking            -0.20667151 0.8132868 0.3019860 -0.68 4.9e-01
TreatmentRaking & Clipping  0.95534162 2.5995585 0.2426422  3.94 8.2e-05

Random effects
 Group Variable  Std Dev    Variance  
 Site  Intercept 0.30795467 0.09483608
 Block Intercept 0.84503192 0.71407895

Let’s compare the the first two models to test for the significance of the term that is removed (using LR)

anova(cox_fullmodel1,cox_fullmodel2) #Not significant
Analysis of Deviance Table
 Cox model: response is  Surv(NumDaysAlive, CensorReproduction)
 Model 1: ~Habitat * Treatment + (1 | Site) + (1 | Block)
 Model 2: ~Habitat + Treatment + (1 | Site) + (1 | Block)
   loglik Chisq Df P(>|Chi|)
1 -1117.4                   
2 -1118.8 2.919  4    0.5715
AIC(cox_fullmodel1,cox_fullmodel2) #Interaction term is not significant and the second model has a lower AIC score. So we can drop the interaction term and keep fullmodel2.

So, now let’s add in population nested under site as a random effect

cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 20 105 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1116.667 -1086.004

                   Chisq    df p    AIC    BIC
Integrated loglik 175.13  8.00 0 159.13 130.86
 Penalized loglik 236.45 22.68 0 191.10 110.96

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment +      (1 | Site/Population) + (1 | Block) 
Fixed coefficients
                                  coef exp(coef)  se(coef)     z       p
HabitatVegetated            0.03971413 1.0405133 0.2256647  0.18 8.6e-01
TreatmentBiomass Removal    1.58216404 4.8654735 0.2279166  6.94 3.9e-12
TreatmentHemizonia         -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking            -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping  0.94413698 2.5705939 0.2466166  3.83 1.3e-04

Random effects
 Group           Variable    Std Dev      Variance    
 Site/Population (Intercept) 0.3669601665 0.1346597638
 Site            (Intercept) 0.0200515483 0.0004020646
 Block           Intercept   0.8757403665 0.7669211894

Now we can compare the models to see which model is best

anova(cox_fullmodel2,cox_fullmodel3) #Significant
Analysis of Deviance Table
 Cox model: response is  Surv(NumDaysAlive, CensorReproduction)
 Model 1: ~Habitat + Treatment + (1 | Site) + (1 | Block)
 Model 2: ~Habitat + Treatment + (1 | Site/Population) + (1 | Block)
   loglik  Chisq Df P(>|Chi|)  
1 -1118.8                      
2 -1116.7 4.3685  1   0.03661 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
AIC(cox_fullmodel2,cox_fullmodel3) #Looks like fullmodel3 is the better model because of the lower AIC score

###Best Model

cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 20 105 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1116.667 -1086.004

                   Chisq    df p    AIC    BIC
Integrated loglik 175.13  8.00 0 159.13 130.86
 Penalized loglik 236.45 22.68 0 191.10 110.96

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment +      (1 | Site/Population) + (1 | Block) 
Fixed coefficients
                                  coef exp(coef)  se(coef)     z       p
HabitatVegetated            0.03971413 1.0405133 0.2256647  0.18 8.6e-01
TreatmentBiomass Removal    1.58216404 4.8654735 0.2279166  6.94 3.9e-12
TreatmentHemizonia         -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking            -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping  0.94413698 2.5705939 0.2466166  3.83 1.3e-04

Random effects
 Group           Variable    Std Dev      Variance    
 Site/Population (Intercept) 0.3669601665 0.1346597638
 Site            (Intercept) 0.0200515483 0.0004020646
 Block           Intercept   0.8757403665 0.7669211894

###Risk Assessment These values are found in the model summary, but if you want to pull them out, here is how you interpret them. 1 = no effect, <1 = decreased risk of death, >1 = increased risk of death.

exp(coef(cox_fullmodel3)) #This should be interpreted that Biomass Removal is almost 5% more likely to survive to reproduction compared to Control, and Raking + Clipping is about 2.5% more likely to survive to reproduction compared to Control.
          HabitatVegetated   TreatmentBiomass Removal         TreatmentHemizonia 
                 1.0405133                  4.8654735                  0.9640041 
           TreatmentRaking TreatmentRaking & Clipping 
                 0.7576054                  2.5705939 
exp(ranef(cox_fullmodel3)$Block)
        A         B         C         D         E         F         G         H         I 
5.9382701 1.0302514 0.5553219 0.7170038 3.1240541 1.0901227 0.7725668 0.8326539 0.4957931 
        J 
0.3779511 
exp(ranef(cox_fullmodel3)$Site) # Pretty even among all the sites
      BAY       CHE       GUA       LEX       OAP       PAR       PEN       SSJ 
1.0018910 0.9997332 0.9997487 1.0001393 0.9987233 1.0021430 0.9986960 0.9989318 
Anova(cox_fullmodel3)
Analysis of Deviance Table (Type II tests)

Response: Surv(NumDaysAlive, CensorReproduction)
          Df  Chisq Pr(>Chisq)    
Habitat    1  0.031     0.8603    
Treatment  4 84.060     <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Looks like roadside and offroad plants are the same, so let’s combine them together in a model (aka, removing the Habitat term)

cox_fullmodel4<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel4)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 22 115 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1116.683 -1086.019

                   Chisq    df p    AIC    BIC
Integrated loglik 175.09  7.00 0 161.09 136.36
 Penalized loglik 236.42 22.35 0 191.73 112.77

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Treatment + (1 | Site/Population) +      (1 | Block) 
Fixed coefficients
                                 coef exp(coef)  se(coef)     z       p
TreatmentBiomass Removal    1.5817104 4.8632667 0.2278906  6.94 3.9e-12
TreatmentHemizonia         -0.0358444 0.9647904 0.2497418 -0.14 8.9e-01
TreatmentRaking            -0.2777768 0.7574659 0.3054173 -0.91 3.6e-01
TreatmentRaking & Clipping  0.9445967 2.5717760 0.2466066  3.83 1.3e-04

Random effects
 Group           Variable    Std Dev      Variance    
 Site/Population (Intercept) 0.3670944471 0.1347583331
 Site            (Intercept) 0.0200501745 0.0004020095
 Block           Intercept   0.8752789365 0.7661132166
summary(cox_fullmodel3)
Cox mixed-effects model fit by maximum likelihood
  Data: mydata
  events, n = 253, 800
  Iterations= 20 105 
                   NULL Integrated    Fitted
Log-likelihood -1204.23  -1116.667 -1086.004

                   Chisq    df p    AIC    BIC
Integrated loglik 175.13  8.00 0 159.13 130.86
 Penalized loglik 236.45 22.68 0 191.10 110.96

Model:  Surv(NumDaysAlive, CensorReproduction) ~ Habitat + Treatment +      (1 | Site/Population) + (1 | Block) 
Fixed coefficients
                                  coef exp(coef)  se(coef)     z       p
HabitatVegetated            0.03971413 1.0405133 0.2256647  0.18 8.6e-01
TreatmentBiomass Removal    1.58216404 4.8654735 0.2279166  6.94 3.9e-12
TreatmentHemizonia         -0.03665974 0.9640041 0.2497694 -0.15 8.8e-01
TreatmentRaking            -0.27759259 0.7576054 0.3053999 -0.91 3.6e-01
TreatmentRaking & Clipping  0.94413698 2.5705939 0.2466166  3.83 1.3e-04

Random effects
 Group           Variable    Std Dev      Variance    
 Site/Population (Intercept) 0.3669601665 0.1346597638
 Site            (Intercept) 0.0200515483 0.0004020646
 Block           Intercept   0.8757403665 0.7669211894
anova(cox_fullmodel3,cox_fullmodel4) #Significant
Analysis of Deviance Table
 Cox model: response is  Surv(NumDaysAlive, CensorReproduction)
 Model 1: ~Habitat + Treatment + (1 | Site/Population) + (1 | Block)
 Model 2: ~Treatment + (1 | Site/Population) + (1 | Block)
   loglik  Chisq Df P(>|Chi|)
1 -1116.7                    
2 -1116.7 0.0319  1    0.8582
AIC(cox_fullmodel3,cox_fullmodel4) #Looks like fullmodel4 is the better model because the AIC score is within 2 points of each other, therefore the models are assessed the same and you should take the simpler model. But this is for another manuscript, probably.

##ggplot ###Interaction Plot

pd<-position_dodge(0)

surv_mydata1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(PropBudSite),
            SD=sd(PropBudSite),
            N=length(PropBudSite))%>%
  mutate(SE=SD/sqrt(N))
`summarise()` has grouped output by 'Treatment'. You can override using the `.groups` argument.
field_int_surv_all_gg<-ggplot(data=surv_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Proportion Survival to Bud")+
  coord_cartesian(ylim=c(0,1))+
  xlab("")+
  theme_classic(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        legend.position = c(0.8, 0.8))+
  scale_y_continuous(name="Proportion Survival to Bud",
                     limits=c(0,1),
                     breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Proportion Survival to Bud",
       fill="Source Habitat",
       color="Source Habitat",
       shape="Source Habitat")
field_int_surv_all_gg

ggsave(plot=field_int_surv_all_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_surv_all_gg.png",width=25,height=15,units="cm",dpi=800)

##autoplot - Survival Curves Plot by habitat test

pd<-position_dodge(0)

cox_model_data1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")

cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=cox_model_data1)

surv_curve_habitat_treat<-ggplot(data=cox_model_graph1)+
  aes(x=time,
      y=surv,
      shape=strata,
      group=strata,
      color=strata)+
 theme_classic(base_size=16)+
  scale_y_continuous(name="Survival Probability",
                     limits=c(0,1),
                     breaks=seq(0.0,1.0,0.1))+
  scale_x_continuous(name="Survival Time (Days)",
                     limits=c(0,275),
                     breaks=seq(0,275,20))+
  geom_step(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  labs(x="Survival Time (Days)",
       y="Survival Probability")

surv_curve_habitat_treat

##autoplot - Survival Curves Plot by habitat

cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=mydata)

autoplot(cox_model_graph1)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme_bw(base_size=16)+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))

Plot by all treatment

cox_model_graph2<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=mydata)

autoplot(cox_model_graph2)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))

Post hoc Tukey

summary(glht(cox_fullmodel4,mcp(Treatment="Tukey")))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: coxme(formula = Surv(NumDaysAlive, CensorReproduction) ~ Treatment + 
    (1 | Site/Population) + (1 | Block), data = mydata)

Linear Hypotheses:
                                         Estimate Std. Error z value Pr(>|z|)    
Biomass Removal - Grassland == 0          1.58171    0.22789   6.941  < 0.001 ***
Hemizonia - Grassland == 0               -0.03584    0.24974  -0.144  0.99990    
Raking - Grassland == 0                  -0.27778    0.30542  -0.909  0.89129    
Raking & Clipping - Grassland == 0        0.94460    0.24661   3.830  0.00117 ** 
Hemizonia - Biomass Removal == 0         -1.61755    0.23484  -6.888  < 0.001 ***
Raking - Biomass Removal == 0            -1.85949    0.28678  -6.484  < 0.001 ***
Raking & Clipping - Biomass Removal == 0 -0.63711    0.21317  -2.989  0.02288 *  
Raking - Hemizonia == 0                  -0.24193    0.31280  -0.773  0.93699    
Raking & Clipping - Hemizonia == 0        0.98044    0.25237   3.885  < 0.001 ***
Raking & Clipping - Raking == 0           1.22237    0.29585   4.132  < 0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

Plot by 2 treatments (Control and Biomass Removal), and adding in Habitat

cox_sub_mydata<-subset(mydata,Treatment%in%c('Control','Biomass Removal'))
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))


cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment+
                            Habitat,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))

---
title: "Relative fitness in a field setting"
output: html_notebook
date: December 2022
authors: Miranda Melen, Nicky Lustenhouwer
---
#Background
At Blue Oak Ranch Reserve, we established a 10m x 26m fenced field site to test whether evolution over the course of invasion away from roads has resulted in enhanced performance in undisturbed vegetation relative to roadside populations. The experiment was replicated in a randomized block design (20 plots in total). Each plot was 1.5m2 with 16 D. graveolens growing in a 4 x 4 grid centered on the plot. There was 33cm between each plant and 25cm between the edge plants and the border of the plot.

The experiment included multiple treatments; however, only the two most relevant to the focus of this paper are included here. We tested whether plant genotypes collected from the two habitats responded differently to the disturbance of biomass removal. We tilled in December 2020 to completely remove below and aboveground biomass, and then weeded to remove aboveground biomass throughout the growing season. In contrast, we left the control plots untouched, allowing the previous year’s thatch to persist and background vegetation to grow throughout the experiment. 

In January 2021, we germinated seeds in Petri dishes at the UCSC Coastal Science Campus greenhouse incubation chambers before transplanting them into field-collected soil (collected in late December 2020 from Blue Oak Ranch Reserve). Seedlings grew in the greenhouse for about eight weeks until all plants had their first two true leaves emerge and lengthen. Ideally, we would have placed seeds directly into the field, but to maximize biosafety, we used seedling transplants that could be tracked with 100% certainty.

We measured the longest leaf for each plant and then transplanted them into the ground in late February 2021 at Blue Oak Ranch Reserve. During the first month of growth, we replaced any D. graveolens that died. We conducted weekly phenology surveys to assess D. graveolens plant health, and at the first sign of buds, we measured plant height and harvested the aboveground biomass by cutting at the root crown and drying in a 60ºC oven for 3 days before weighing.

#Data Analysis
Statistical analyses were performed in R version 4.2.1 (R Core Team 2022) using linear mixed-effects models with the lme4 (Bates et al. 2015), lmerTest (Kuznetsova et al. 2017), and DHARMa packages (Hartig 2022), generalized linear mixed models with the glmmTMB package (Brooks et al. 2017), and mixed effects cox models with the coxme (Therneau 2022a) and survival (Therneau 2022b) packages.

##Models Included

###Cox proportional hazard models
Here we will use 'coxme' which allows you to conduct mixed effects Cox proportional hazards models. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. We conducted a germination experiment using Dittrichia graveolens seeds on filter paper. "Surv" creates a survival object to combine the days column (NumDaysAlive) and the censor column (Censor) to be used as a response variable in a model formula.The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: "var1 * var2" will give you the interaction term and the individual variables: so "var1 + var2 + var1:var2". To add random effects, type "+ (1|random effect)".

Assumptions for cox models: https://www.theanalysisfactor.com/assumptions-cox-regression/

###Linear mixed-effects models
Linear Mixed Effects models are used for regression analyses involving dependent data. For a tutorial: https://doi.org/10.1177/2515245920960351

fullmodel<-lmer(log(Biomass)~   #Response variable: biomass
          Habitat*Treatment+    #Fixed effects and their interactions(*)
          (1|Site)+(1|Block),   #Random effect with random intercept only
          data=mydata)             #Dataframe
  
###Generalized linear mixed models
under construction


#Libraries
```{r}
#install.packages("coxme")
#install.packages("survival")
#install.packages("ggplot2")
#install.packages("ggfortify")
#install.packages("car")
#install.packages("multcomp")
#install.packages("lme4")
#install.packages("lmerTest")
#install.packages("DHARMa")
#install.packages("dplyr")
#install.packages("emmeans")
#install.packages('TMB', type = 'source')
#install.packages("glmmTMB")
#install.packages("MASS")
#install.packages("emmeans")
#install.packages("AICcmodavg")
library(coxme)
library(survival)
library(ggplot2)
library(ggfortify)
library(car)
library(multcomp)
library(lme4)
library(lmerTest)
library(DHARMa)
library(dplyr)
library(emmeans)
library(TMB)
library(glmmTMB)
library(MASS)
library(emmeans)
library(AICcmodavg)
```

#Load Data
This dataframe has one row per plant (800 observations). Data are for survivorship curves (3 censor options), the number of days the plant stayed alive (NumDaysAlive) and aboveground biomass. Censors with a 1 denote reaching the event (CensorAll = died, CensorBiomass = survived to collect biomass, CensorReproduction = survived to reproduce) and a 0 denoting when a seed didn't germinate by the last census date (Census = 11/15/21). CensorReproduction will be most useful in understanding the amount of biomass produced by an individual when buds appear.
```{r}
mydata<-read.csv("/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/data/field_relative_fitness/field_relative_fitness_2021.csv",stringsAsFactors=T)
str(mydata) #Check that each column has the right class (factor, integer, numeric, etc.)
mydata$Site<-as.character(mydata$Site)
mydata$Treatment<-factor(mydata$Treatment,
                         levels=c("Grassland","Biomass Removal","Hemizonia","Raking","Raking & Clipping")) #Changing the contrast order so that everything is compared to Grassland (control)
```

#Early Growth
This code uses Growth data with Habitat (roadside and vegetated) and Treatment in a glmm model. Anova and Tukey tests are used on the successful Model4 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Growth data is a measure of growth of longest leaf. Each plant was measured upon transplanting into the field in March and then again in June 2021. This plant has a juvenile stage of a basal rosette, and then it bolts and produced smaller cauline leaves. The goal was to capture early growth data for this plant before bolting occurs so that the measurements capture the basal rosette stage, however in some cases the plants bolted earlier than expected and the resulting measurement was smaller than previous. In these cases we determined that the data should be removed from the dataset as the negative number (or changing it to a zero) does not reflect the biological importance of the measurement.

Here we need to filter the data to remove Growth>0 and to convert plant measurement dates to date format
```{r}
growth_mydata<-mydata%>%filter(Growth>0) #Here I am only looking at the Growth data that is greater than 0 (see Also Note above)
growth_mydata$PlantDate<-as.Date(growth_mydata$PlantDate,"%m/%d/%y") 
growth_mydata$Num.Days.Growth<-as.Date("2021-05-22")-growth_mydata$PlantDate
growth_mydata$Num.Days.Growth<-as.numeric(growth_mydata$Num.Days.Growth)
growth_mydata$Growth.Rate<-growth_mydata$Growth/growth_mydata$Num.Days.Growth #First calculate number of days of growth to get the Rate (Growth/Num.Days.Growing). Then fit data to Beta distribution
```

##Histograms
Original data
```{r}
hist(growth_mydata$Growth.Rate,
     col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(growth_mydata$Growth.Rate)
```

Log transform data (https://www.statology.org/transform-data-in-r/)
```{r}
log_growth_mydata<-log10(growth_mydata$Growth.Rate)
hist(log_growth_mydata,
     col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_growth_mydata) #Data does not improve with log transformation
```

Next I tried a square root transformation, which improved the distribution, but my first model failed the singular fit (see Model 1).
```{r}
sqrt_growth_mydata<-sqrt(growth_mydata$Growth.Rate)
hist(sqrt_growth_mydata,
     col='steelblue',main='Log Transformed') #Square root transformed data looks better than the original distribution
shapiro.test(sqrt_growth_mydata)
```

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
```{r}
growth_fullmodel1<-lmer(sqrt(Growth.Rate)~Habitat*Treatment+(1|Site)+(1|Block),data=growth_mydata)
isSingular(growth_fullmodel1,tol=1e-4) #=True
summary(growth_fullmodel1) #Variance explained by Site = 0.000
anova(growth_fullmodel1)
```

Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to see if it adds to the model.

##Model 2 - lmer: Modeling Site as a fixed effect and Block as a random effect
```{r}
growth_fullmodel2<-lmer(log(Growth.Rate)~
                          Habitat*Treatment+
                          Site+
                          (1|Block),
                        data=growth_mydata)
summary(growth_fullmodel2) #As a fixed effect, one of the Sites (OAP) is significant.
anova(growth_fullmodel2) #Site as a fixed effect accounts for 3% of the variance
```

Now we'll look at the QQ plots and the residuals using DHARMa
```{r}
qqnorm(resid(growth_fullmodel2)) #qqplot
qqline(resid(growth_fullmodel2)) #add the line
testDispersion(growth_fullmodel2) #red line should be in the middle of the distribution
myDHARMagraph2<-simulateResiduals(growth_fullmodel2) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph2) #plotting graph. At this point, you don't want any text or lines to be red. The QQ plots are not looking good for this model. Let's try fitting to a negative binomial distribution.
```

##Model 3 - glmer.nb: Negative Binomial
```{r eval=FALSE, include=FALSE}
growth_fullmodel3<-glmer.nb(Growth.Rate~
                              Habitat*Treatment+
                              (1|Site)+
                              (1|Block),
                            data=growth_mydata) #this barfs right now, not sure why
isSingular(growth_fullmodel3,
           tol=1e-4)
#summary(growth_fullmodel3)
#qqnorm(resid(growth_fullmodel3)) #qqplot
#qqline(resid(growth_fullmodel3)) #add the line
#testDispersion(growth_fullmodel3) #red line should be in the middle of the distribution
#myDHARMagraph3<-simulateResiduals(growth_fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
#plot(myDHARMagraph3) #plotting graph.The QQ plots are not looking good for this model.
```

So this model isn't working well either. Let's try building a model and fitting it to a Beta distribution, first without a link function. Check it with DHARMa, and if it doesn't look good, then fit it to a Beta distribution with a logit link function.

##Model 4 - glmm: Beta Distribution
Now I'm using glmm because I'm fitting to other distributions (beta)
```{r}
growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata) 
summary(growth_fullmodel4)
qqnorm(resid(growth_fullmodel4)) #qqplot
qqline(resid(growth_fullmodel4)) #add the line
testDispersion(growth_fullmodel4) #red line should be in the middle of the distribution
myDHARMagraph4<-simulateResiduals(growth_fullmodel4) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph4) #plotting graph. Looks good, but check the outliers to make sure they are real.
```

We should check the outliers in the the DHARMa plot to make sure they make sense.
```{r}
growth_outlier_boxplot1<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Habitat,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Habitat")
growth_outlier_boxplot1

max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Away"])
max(growth_mydata$Growth.Rate[growth_mydata$Habitat=="Roadside"]) #Yes, these outliers make sense, so I don't need to worry about the red stars in the DHARMa plot.
```

###Best Model
```{r}
growth_fullmodel4<-glmmTMB(Growth.Rate~
                             Habitat*Treatment+
                             (1|Site)+
                             (1|Block),
                           family=beta_family(),
                           data=growth_mydata)
summary(growth_fullmodel4)
```

###Post-Hoc Test
Remove non-significant interaction terms before running the Tukey.
```{r}
#?emmeans,emmeans(model,pairwise~treatment)
growth_fullmodel4.1<-glmmTMB(Growth.Rate~
                               Treatment+
                               (1|Site)+
                               (1|Block),
                             family=beta_family(),
                             data=growth_mydata)
summary(growth_fullmodel4.1)
emmeans(growth_fullmodel4.1,
        pairwise~Treatment)
```

###ggplot
####Interaction plot
#####Biomass x Treatment: Growth
```{r}
pd<-position_dodge(0)

growth_mydata1<-growth_mydata%>% 
  replace_na(list(Biomass=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Biomass),
            SD=sd(Biomass),
            N=length(Biomass))%>%
  mutate(SE=SD/sqrt(N))

field_int_bio_grow_gg<-ggplot(growth_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Biomass (g)")+
  coord_cartesian(ylim = c(0,10))+
  xlab("")+
  #ggtitle("Biomass of Dittrichia graveolens that budded")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="*Dittrichia graveolens* Biomass (g)",
       color="Source Habitat",
       shape="Source Habitat")
field_int_bio_grow_gg

ggsave(plot=field_int_bio_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_bio_grow_gg.png",width=25,height=15,units="cm",dpi=800)
```

#####Growth Rate x Treatment: Growth
```{r}
pd<-position_dodge(0)

growth_mydata2<-growth_mydata%>% 
  replace_na(list(Growth.Rate=0))%>%
  filter(CensorReproduction==1)%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(Growth.Rate),
            SD=sd(Growth.Rate),
            N=length(Growth.Rate))%>%
  mutate(SE=SD/sqrt(N))

field_int_rate_grow_gg<-ggplot(growth_mydata2,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  coord_cartesian(ylim = c(0,0.5))+
  xlab("")+
  theme_bw(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        axis.title.y=ggtext::element_markdown(),
        legend.position = c(0.8, 0.8))+
 # ggtitle("Growth of Dittrichia graveolens that budded")+
  ylab("*Dittrichia graveolens* \n (Change in Leaf Length/Days)")+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Change in *Dittrichia graveolens* Leaf Length/Day",
       color="Source Habitat",
       shape="Source Habitat")
field_int_rate_grow_gg

ggsave(plot=field_int_rate_grow_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_rate_grow_gg.png",width=25,height=15,units="cm",dpi=800)
```


####Boxplot
```{r eval=FALSE, include=FALSE}
#Boxplot with Early Growth Rate by Habitat
growth_boxplot1<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Habitat,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray58"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Habitat")
growth_boxplot1

ggsave(plot=growth_boxplot1,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot1.png",width=20,height=20,units="cm",dpi=800)

#Boxplot with Early Growth Rate by All Treatments
growth_boxplot2<-ggplot(growth_mydata)+
  geom_boxplot(aes(x=Treatment,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Treatment")
growth_boxplot2

ggsave(plot=growth_boxplot2,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplot2.png",width=20,height=20,units="cm",dpi=800)

#Boxplot with Early Growth Rate by 2 treatments (Control and Biomass Removal)
sub_growth_mydata<-subset(growth_mydata,
                          Treatment%in%c('Control','Biomass Removal'))

growth_boxplot3<-ggplot(sub_growth_mydata)+
  geom_boxplot(aes(x=Treatment,
                   y=Growth.Rate),
               size=0.5)+
  theme_classic()+
  ylim(0,1)+
  scale_fill_manual(values=c("forestgreen","gray85"))+
  labs(y="Early Growth Rate",
       fill="Habitat",
       x="Treatment")
growth_boxplot3

#ggsave(plot=growth_boxplot3,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/growth_boxplo3.png",width=20,height=20,units="cm",dpi=800)
```

#Reproductive Biomass
This code uses Biomass data with Habitat (roadside and vegetated) and Treatment in a lmer model. ANOVA and Tukey are used on the successful Model 3 with the creation of a box plot as a finished product.

Note: 10 blocks, 5 treatments, 16 populations (CHE-O), 8 sites (random: population pairs; CHE), 2 habitat (fixed effect: roadside and vegetated; R or O)

Also Note: Biomass data is a measure of aboveground biomass of all individuals harvested from the site. In some cases this was before they budded, but we harvested the wilted plant in case that information was needed in the future. Here we only want to look at biomass (well, log(Biomass)) for reproductive individuals.

Here we need to subset the data to only look at Biomass when CensorReproduction = 1, so that all Biomass data is for reproductive individuals only.
```{r}
reproduction_mydata<-subset(mydata,CensorReproduction%in%c('1')) #Here I am only looking at the Biomass data where the CensorReproduction = 1
```


##Histograms
```{r}
hist(reproduction_mydata$Biomass,col='steelblue',main='Original') #Original data is skewed, let's test for normality and consider log transforming the data
shapiro.test(reproduction_mydata$Biomass)
```

Log transform data (https://www.statology.org/transform-data-in-r/)
```{r}
log_reproduction_mydata<-log10(reproduction_mydata$Biomass)
hist(log_reproduction_mydata,col='steelblue',main='Log Transformed') #Log transformed data, this looks better than the original distribution
shapiro.test(log_reproduction_mydata)
#Log transformed data has a better distribution than the original data so we will use the log transformed data with our models
```

##Model 1 - lmer: Modeling Habitat and Treatment with Site and Block as random effects
```{r}
fullmodel1<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Site)+
                   (1|Block),
                 data=reproduction_mydata)
isSingular(fullmodel1,tol=1e-4)
summary(fullmodel1) #Variance explained by Site = 0.000
anova(fullmodel1)
#Site as a random effect does not explain any of the variance in the model, therefore let's try Site as a fixed effect to demonstrate that it doesn't add to the model.
predict(fullmodel1)
hist(predict(fullmodel1,type="response"))
```

##Model 2 - lmer: Modeling Site as a fixed effect
```{r}
fullmodel2<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   Site+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel2)
anova(fullmodel2)
#Site as a fixed effect is not significant, therefore it should not be used as a fixed effect in addition to it not being used as a random effect.
```

##Model 3 - lmer: Site is removed from this model because it explains very little of the variance
```{r}
fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
anova(fullmodel3)
```

###QQ Plots
Now we'll look at the QQ plots and the residuals using DHARMa
```{r}
qqnorm(resid(fullmodel3)) #qqplot
qqline(resid(fullmodel3)) #add the line
testDispersion(fullmodel3) #red line should be in the middle of the distribution
myDHARMagraph3<-simulateResiduals(fullmodel3) #making a graph using DHARMa package, also testing for heteroscedasticity, https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#heteroscedasticity
plot(myDHARMagraph3) #plotting graph. At this point, you don't want any text or lines to be red.
```

When we compare the model summaries and Anova of Models 2 & 3, we see that the removal of Site doesn't impact the model. Therefore the more simple Model 3 is a better choice. But let's keep checking...

###Compare AIC Scores
Now I need to compare the AIC scores for all the models to tell me which is the better model (but it will not say which fits my data better, that is why I did all the DHARMa stuff)

Using aictab to make the comparison of models and table 
```{r}
models<-list(fullmodel1,fullmodel2,fullmodel3)
mod.names<-c('Site.Random','Site.Fixed','No.Site')
aictab(cand.set=models,modnames=mod.names)
#The lowest AICc score is listed first and indicates the best fitting model, here, Model 3 (No.Site) where Site is not included. The cut-off for comparing Models is 2 units. The difference between Model 1 (Site.Random) and Model 3 (No.Site) is 2.12. So Model 3 is marginally better than Model 2.
```

##Other Models Attempted
Other ideas that were explored to keep Site but resulted in singular error and Site explaining 0.000 of the variance: 
fullmodel4<-lmer(log(Biomass)~ Treatment + (1|Site) + (1|Block), data = mydata)
summary(fullmodel4)
fullmodel5<-lmer(log(Biomass)~ Treatment + Habitat + (1|Site) + (1|Block), data = mydata)
summary(fullmodel5)

###Best Model
```{r}
fullmodel3<-lmer(log(Biomass)~
                   Habitat*Treatment+
                   (1|Block),
                 data=reproduction_mydata)
summary(fullmodel3)
```

###Post-Hoc Test
You should remove non-significant interaction terms before running a post-hoc test. It is difficult to judge the main effects (Habitat and Site) when you also have the interaction term present, so when it is not significant, fit a new model without it.
```{r}
fullmodel3.1<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block),
                   data=reproduction_mydata)
summary(fullmodel3.1)
anova(fullmodel3.1)
qqnorm(resid(fullmodel3.1)) #qqplot
qqline(resid(fullmodel3.1)) #add the line
testDispersion(fullmodel3.1) #red line should be in the middle of the distribution
myDHARMagraph3.1<-simulateResiduals(fullmodel3.1) #testing for heteroscedasticity
plot(myDHARMagraph3.1) #plotting graph
```

```{r}
fullmodel3.2<-lmer(log(Biomass)~
                     Treatment+
                     (1|Block)+
                     (1|Population),
                   data=mydata)
summary(fullmodel3.2)
anova(fullmodel3.2)
qqnorm(resid(fullmodel3.2)) #qqplot
qqline(resid(fullmodel3.2)) #add the line
testDispersion(fullmodel3.2) #red line should be in the middle of the distribution
myDHARMagraph3.2<-simulateResiduals(fullmodel3.2) #testing for heteroscedasticity
plot(myDHARMagraph3.2) #plotting graph
```

Let's use emmeans for our Best Model (fullmodel3) and the two off-shoots.
```{r}
#?emmeans, emmeans(model, pairwise ~ treatment)
emmeans(fullmodel3,pairwise~Treatment)
emmeans(fullmodel3.1,pairwise~Treatment)
emmeans(fullmodel3.2,pairwise~Treatment)
#These models result in similar Tukey outcomes
```

##ggplot - lmer
```{r}
ggplot(data=reproduction_mydata,
       aes(x=Treatment,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)
ggplot(data=reproduction_mydata,
       aes(x=Habitat,
           y=log(Biomass)))+
  geom_boxplot() #plot data from log(data)
```

#Survival Analysis
This code uses NumDaysAlive data with Habitat (roadside and vegetated) and Treatment in a Cox proportional hazards model to assess survival. Information here: https://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. "Surv" creates a survival object to combine the days column (NumDaysAlive) and the reproductive censor column (ReproductionCensor) to be used as a response variable in a model formula. The model follows the same syntax as linear models (lm, lmer, etc). Fixed effects: "var1 * var2" will give you the interaction term and the individual variables: so "var1 + var2 + var1:var2". To add random effects, type "+ (1|random effect)". 

##Histograms
```{r}
#Number of Days Alive - All data
hist(mydata$NumDaysAlive,col='steelblue',main='Original') 

#Number of Days Alive - By Habitat
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Habitat)) #Here we see that both Habitats have the same bi-modal distribution.

#Number of Days Alive - By Treatment
ggplot(mydata,aes(x=NumDaysAlive))+geom_histogram()+facet_wrap(vars(Treatment)) #Here we see that 4 of the treatments have the same bi-modal distribution and Biomass Removal is right skewed.
```

##Cox Model
Start by making a simple model with no random effects. This will be compared to the full model with random effects.
```{r}
cox_simplemodel1<-coxph(Surv(NumDaysAlive,
                             CensorReproduction)~
                          Habitat*Treatment,
                        data=mydata)
summary(cox_simplemodel1)
print(cox_simplemodel1)
predict(cox_simplemodel1)
hist(predict(cox_simplemodel1))
```

Now make a full model using random effects
```{r}
cox_fullmodel1<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat*Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata)
summary(cox_fullmodel1)
print(cox_fullmodel1)
predict(cox_fullmodel1)
hist(predict(cox_fullmodel1))
```

Now we can compare the models to see which model is best
```{r}
anova(cox_simplemodel1,cox_fullmodel1) 
#See example: https://www.rdocumentation.org/packages/coxme/versions/2.2-16/topics/coxme
AIC(cox_simplemodel1,cox_fullmodel1) #But comparing the AIC scores is easiest. Keep the lower AIC score because that is considered the better model. Here it is the fullmodel1.
```

Now, let's make a model with no interaction term
```{r}
cox_fullmodel2<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel2)
```

Let's compare the the first two models to test for the significance of the term that is removed (using LR)
```{r}
anova(cox_fullmodel1,cox_fullmodel2) #Not significant
AIC(cox_fullmodel1,cox_fullmodel2) #Interaction term is not significant and the second model has a lower AIC score. So we can drop the interaction term and keep fullmodel2.
```

So, now let's add in population nested under site as a random effect
```{r}
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
```

Now we can compare the models to see which model is best
```{r}
anova(cox_fullmodel2,cox_fullmodel3) #Significant
AIC(cox_fullmodel2,cox_fullmodel3) #Looks like fullmodel3 is the better model because of the lower AIC score
```

###Best Model
```{r}
cox_fullmodel3<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Habitat+
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel3)
```

###Risk Assessment
These values are found in the model summary, but if you want to pull them out, here is how you interpret them.
1 = no effect, <1 = decreased risk of death, >1 = increased risk of death.
```{r}
exp(coef(cox_fullmodel3)) #This should be interpreted that Biomass Removal is almost 5% more likely to survive to reproduction compared to Control, and Raking + Clipping is about 2.5% more likely to survive to reproduction compared to Control.
exp(ranef(cox_fullmodel3)$Block)
exp(ranef(cox_fullmodel3)$Site) # Pretty even among all the sites
Anova(cox_fullmodel3)
```

Looks like roadside and offroad plants are the same, so let's combine them together in a model (aka, removing the Habitat term)
```{r}
cox_fullmodel4<-coxme(Surv(NumDaysAlive,CensorReproduction)~
                        Treatment+
                        (1|Site/Population)+
                        (1|Block),
                      data=mydata) 
summary(cox_fullmodel4)
summary(cox_fullmodel3)
anova(cox_fullmodel3,cox_fullmodel4) #Significant
AIC(cox_fullmodel3,cox_fullmodel4) #Looks like fullmodel4 is the better model because the AIC score is within 2 points of each other, therefore the models are assessed the same and you should take the simpler model. But this is for another manuscript, probably.
```

##ggplot
###Interaction Plot

```{r}
pd<-position_dodge(0)

surv_mydata1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")%>%
  group_by(Treatment,Habitat)%>%
  summarise(Mean=mean(PropBudSite),
            SD=sd(PropBudSite),
            N=length(PropBudSite))%>%
  mutate(SE=SD/sqrt(N))

field_int_surv_all_gg<-ggplot(data=surv_mydata1,
                        aes(x=factor(Treatment,levels=c("Biomass Removal","Grassland")),
                            y=Mean,
                            shape=Habitat,
                            group=Habitat,
                            color=Habitat))+
  ylab("Proportion Survival to Bud")+
  coord_cartesian(ylim=c(0,1))+
  xlab("")+
  theme_classic(base_size=16)+
  theme(panel.border=element_blank(),
        panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        axis.line=element_line(colour="black"),
        legend.position = c(0.8, 0.8))+
  scale_y_continuous(name="Proportion Survival to Bud",
                     limits=c(0,1),
                     breaks=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))+
  geom_line(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  geom_errorbar(width=0.15,
                position=pd,
                aes(ymin=(Mean-SE),
                    ymax=(Mean+SE)))+
  labs(y="Proportion Survival to Bud",
       fill="Source Habitat",
       color="Source Habitat",
       shape="Source Habitat")
field_int_surv_all_gg

ggsave(plot=field_int_surv_all_gg,file="/Users/Miranda/Documents/Education/UC Santa Cruz/Dittrichia/Dittrichia_Analysis/figures/field_int_surv_all_gg.png",width=25,height=15,units="cm",dpi=800)
```

##autoplot - Survival Curves
Plot by habitat test
```{r}
pd<-position_dodge(0)

cox_model_data1<-mydata%>%
  filter(Treatment=="Biomass Removal"|Treatment=="Grassland")

cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=cox_model_data1)

surv_curve_habitat_treat<-ggplot(data=cox_model_graph1)+
  aes(x=time,
      y=surv,
      shape=strata,
      group=strata,
      color=strata)+
 theme_classic(base_size=16)+
  scale_y_continuous(name="Survival Probability",
                     limits=c(0,1),
                     breaks=seq(0.0,1.0,0.1))+
  scale_x_continuous(name="Survival Time (Days)",
                     limits=c(0,275),
                     breaks=seq(0,275,20))+
  geom_step(size=1,
            position=pd)+
  geom_point(size=5,
             position=pd)+
  scale_color_manual(values=c("gray85","forestgreen"))+
  labs(x="Survival Time (Days)",
       y="Survival Probability")

surv_curve_habitat_treat
```

##autoplot - Survival Curves
Plot by habitat
```{r}
cox_model_graph1<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Habitat,
                          data=mydata)

autoplot(cox_model_graph1)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme_bw(base_size=16)+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))
```

Plot by all treatment
```{r}
cox_model_graph2<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=mydata)

autoplot(cox_model_graph2)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))
```
Post hoc Tukey
```{r}
summary(glht(cox_fullmodel4,mcp(Treatment="Tukey")))
```

Plot by 2 treatments (Control and Biomass Removal), and adding in Habitat
```{r}
cox_sub_mydata<-subset(mydata,Treatment%in%c('Control','Biomass Removal'))
cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))

cox_model_graph3<-survfit(Surv(NumDaysAlive,CensorReproduction)~
                            Treatment+
                            Habitat,
                          data=cox_sub_mydata)

autoplot(cox_model_graph3)+
  labs(x="\n Survival Time (Days)",
       y="Survival Probabilities\n",
       title="Survival Times Of \n Roadside and Vegetated Populations\n")+
  theme(plot.title=element_text(hjust=0.5),
        axis.title.x=element_text(face="bold",
                                  color="Black",
                                  size=12),
        axis.title.y=element_text(face="bold",
                                  color="Black",
                                  size=12),
        legend.title=element_text(face="bold",
                                  size=10))
```